Overview

Brought to you by YData

Dataset statistics

Number of variables61
Number of observations491661
Missing cells16712188
Missing cells (%)55.7%
Duplicate rows54161
Duplicate rows (%)11.0%
Total size in memory225.5 MiB
Average record size in memory481.0 B

Variable types

Categorical15
Numeric25
Unsupported2
Boolean5
Text8
DateTime6

Alerts

17 - Юр. лица, имеющие задолженность по уплате налогов has constant value "Нет" Constant
17 - Юр. лица, не предоставляющие налоговую отчетность более года has constant value "Нет" Constant
4 - Перечень ОАО по Распоряжению Правительства № 91-Р has constant value "Нет" Constant
4 - Перечень ФГУП, имеющих существенное значение has constant value "Нет" Constant
4 - Реестр оборонно-промышленного комплекса has constant value "Нет" Constant
4 - Реестр операторов, осуществляющих обработку персональных данных has constant value "Нет" Constant
Количество филиалов has constant value "0.0" Constant
Компания входит в: Юридические лица, в состав исполнительных органов которых входят дисквалифицированные лица has constant value "Нет" Constant
Dataset has 54161 (11.0%) duplicate rowsDuplicates
25 - Государственные заказы и контракты Количество заключенных контрактов is highly overall correlated with 25 - Государственные заказы и контракты Приняла участие (количество) and 2 other fieldsHigh correlation
25 - Государственные заказы и контракты Приняла участие (количество) is highly overall correlated with 25 - Государственные заказы и контракты Количество заключенных контрактов and 2 other fieldsHigh correlation
25 - Коммерческие заказы и контракты Количество заключенных контрактов is highly overall correlated with 25 - Коммерческие заказы и контракты Приняла участие (количество) and 1 other fieldsHigh correlation
25 - Коммерческие заказы и контракты Приняла участие (количество) is highly overall correlated with 25 - Коммерческие заказы и контракты Количество заключенных контрактов and 1 other fieldsHigh correlation
6 - Организация, первое лицо, учредители входит в список «дисквалифицированных» is highly overall correlated with 25 - Государственные заказы и контракты Количество заключенных контрактов and 1 other fieldsHigh correlation
7 - массовый контактный телефон: в заявке указан телефон, на который зарегистрировано более Х компаний (кроме компаний-агентов) is highly overall correlated with Тип организацииHigh correlation
8 - Количество компаний с аналогичным директором is highly overall correlated with 8 - Количество компаний с аналогичным директором в том же регионе and 2 other fieldsHigh correlation
8 - Количество компаний с аналогичным директором в том же регионе is highly overall correlated with 8 - Количество компаний с аналогичным директором and 1 other fieldsHigh correlation
IdInquiry is highly overall correlated with month and 1 other fieldsHigh correlation
has_products is highly overall correlated with КредитыHigh correlation
month is highly overall correlated with IdInquiry and 1 other fieldsHigh correlation
no_reports is highly overall correlated with 25 - Государственные заказы и контракты Количество заключенных контрактов and 3 other fieldsHigh correlation
Индекс должной осмотрительности is highly overall correlated with Индекс должной осмотрительности описание and 2 other fieldsHigh correlation
Индекс должной осмотрительности описание is highly overall correlated with Индекс должной осмотрительности and 1 other fieldsHigh correlation
Индекс финансового риска is highly overall correlated with Индекс финансового риска описание and 2 other fieldsHigh correlation
Индекс финансового риска описание is highly overall correlated with Индекс финансового риска and 1 other fieldsHigh correlation
Количество компаний с аналогичным директором в том же регионе is highly overall correlated with 8 - Количество компаний с аналогичным директором and 1 other fieldsHigh correlation
Количество компаний, зарегистрированных на адресе регистрации Организации по данным сайта ФНС is highly overall correlated with Тип организацииHigh correlation
Количество соучредителей is highly overall correlated with Тип организацииHigh correlation
Кредиты is highly overall correlated with has_productsHigh correlation
Отчетный период (год) is highly overall correlated with Тип организацииHigh correlation
Пассивы всего is highly overall correlated with Индекс финансового риска and 2 other fieldsHigh correlation
Сумма налога is highly overall correlated with Пассивы всего and 2 other fieldsHigh correlation
Тип организации is highly overall correlated with 7 - массовый контактный телефон: в заявке указан телефон, на который зарегистрировано более Х компаний (кроме компаний-агентов) and 13 other fieldsHigh correlation
Участие в госконтрактах (количество) is highly overall correlated with 25 - Коммерческие заказы и контракты Количество заключенных контрактов and 1 other fieldsHigh correlation
Численность компании is highly overall correlated with Тип организации and 1 other fieldsHigh correlation
Чистая прибыль (или убыток) компании is highly overall correlated with Индекс должной осмотрительности and 2 other fieldsHigh correlation
размер уставного капитал ЮЛ is highly overall correlated with Тип организации and 1 other fieldsHigh correlation
2 - В учредителях/участниках/ акционерах клиента участие государства более 50 is highly imbalanced (99.9%) Imbalance
6 - Организация, первое лицо, учредители входит в список «дисквалифицированных» is highly imbalanced (98.5%) Imbalance
has_products is highly imbalanced (75.6%) Imbalance
Индекс должной осмотрительности описание is highly imbalanced (62.5%) Imbalance
Кредиты is highly imbalanced (69.7%) Imbalance
Продукт ранее закрыт is highly imbalanced (90.1%) Imbalance
Численность компании is highly imbalanced (54.7%) Imbalance
17 - Юр. лица, имеющие задолженность по уплате налогов has 298459 (60.7%) missing values Missing
17 - Юр. лица, не предоставляющие налоговую отчетность более года has 298459 (60.7%) missing values Missing
2 - В учредителях/участниках/ акционерах клиента участие государства более 50 has 295964 (60.2%) missing values Missing
25 - Государственные заказы и контракты Количество заключенных контрактов has 468273 (95.2%) missing values Missing
25 - Государственные заказы и контракты Приняла участие (количество) has 468273 (95.2%) missing values Missing
25 - Коммерческие заказы и контракты Количество заключенных контрактов has 472114 (96.0%) missing values Missing
25 - Коммерческие заказы и контракты Приняла участие (количество) has 472114 (96.0%) missing values Missing
4 - Перечень ОАО по Распоряжению Правительства № 91-Р has 298459 (60.7%) missing values Missing
4 - Перечень ФГУП, имеющих существенное значение has 298459 (60.7%) missing values Missing
4 - Реестр оборонно-промышленного комплекса has 298459 (60.7%) missing values Missing
4 - Реестр операторов, осуществляющих обработку персональных данных has 298459 (60.7%) missing values Missing
6 - Организация, первое лицо, учредители входит в список «дисквалифицированных» has 295964 (60.2%) missing values Missing
7 - массовый контактный телефон: в заявке указан телефон, на который зарегистрировано более Х компаний (кроме компаний-агентов) has 379036 (77.1%) missing values Missing
8 - Количество компаний с аналогичным директором has 379036 (77.1%) missing values Missing
8 - Количество компаний с аналогичным директором в том же регионе has 298883 (60.8%) missing values Missing
Выручка компании (млн, руб) has 438555 (89.2%) missing values Missing
Дата блокировки has 427115 (86.9%) missing values Missing
Дата закрытия has 374958 (76.3%) missing values Missing
Дата заявки_report has 296090 (60.2%) missing values Missing
Дата открытия has 84344 (17.2%) missing values Missing
Дата регистрации has 298505 (60.7%) missing values Missing
Даты внесения соучредителей has 379758 (77.2%) missing values Missing
Индекс должной осмотрительности has 381160 (77.5%) missing values Missing
Индекс должной осмотрительности описание has 378993 (77.1%) missing values Missing
Индекс финансового риска has 385774 (78.5%) missing values Missing
Индекс финансового риска описание has 378993 (77.1%) missing values Missing
История смены сооучредителей has 379758 (77.2%) missing values Missing
Количество видов деятельности у Клиента has 298770 (60.8%) missing values Missing
Количество компаний с аналогичным директором в том же регионе has 298883 (60.8%) missing values Missing
Количество компаний, зарегистрированных на адресе регистрации Организации по данным сайта ФНС has 379036 (77.1%) missing values Missing
Количество соучредителей has 379036 (77.1%) missing values Missing
Количество филиалов has 379036 (77.1%) missing values Missing
Компания входит в: Юридические лица, в состав исполнительных органов которых входят дисквалифицированные лица has 298459 (60.7%) missing values Missing
Кредиты has 295964 (60.2%) missing values Missing
ОПФ Организации has 298459 (60.7%) missing values Missing
Отчетный период (год) has 447784 (91.1%) missing values Missing
Пассивы всего has 448457 (91.2%) missing values Missing
Субъект местонахождения has 296248 (60.3%) missing values Missing
Сумма налога has 464390 (94.5%) missing values Missing
Участие в госконтрактах (год) has 472114 (96.0%) missing values Missing
Участие в госконтрактах (количество) has 472114 (96.0%) missing values Missing
Численность компании has 444225 (90.4%) missing values Missing
Чистая прибыль (или убыток) компании has 454534 (92.4%) missing values Missing
дата начала полномочий руководителя has 379312 (77.1%) missing values Missing
код основного оквэд has 298779 (60.8%) missing values Missing
размер уставного капитал ЮЛ has 380788 (77.4%) missing values Missing
25 - Государственные заказы и контракты Количество заключенных контрактов is highly skewed (γ1 = 31.8203907) Skewed
25 - Государственные заказы и контракты Приняла участие (количество) is highly skewed (γ1 = 21.62623264) Skewed
7 - массовый контактный телефон: в заявке указан телефон, на который зарегистрировано более Х компаний (кроме компаний-агентов) is highly skewed (γ1 = 31.37740661) Skewed
Количество компаний, зарегистрированных на адресе регистрации Организации по данным сайта ФНС is highly skewed (γ1 = 33.07340503) Skewed
Количество соучредителей is highly skewed (γ1 = 207.0984734) Skewed
Пассивы всего is highly skewed (γ1 = 54.13233985) Skewed
Сумма налога is highly skewed (γ1 = 30.90619745) Skewed
Чистая прибыль (или убыток) компании is highly skewed (γ1 = 59.17251882) Skewed
размер уставного капитал ЮЛ is highly skewed (γ1 = 329.107981) Skewed
Month is an unsupported type, check if it needs cleaning or further analysis Unsupported
Дата заявки is an unsupported type, check if it needs cleaning or further analysis Unsupported
25 - Государственные заказы и контракты Количество заключенных контрактов has 7653 (1.6%) zeros Zeros
25 - Коммерческие заказы и контракты Количество заключенных контрактов has 8239 (1.7%) zeros Zeros
7 - массовый контактный телефон: в заявке указан телефон, на который зарегистрировано более Х компаний (кроме компаний-агентов) has 66623 (13.6%) zeros Zeros
8 - Количество компаний с аналогичным директором в том же регионе has 80639 (16.4%) zeros Zeros
weekday has 94078 (19.1%) zeros Zeros
Заявки до даты has 290974 (59.2%) zeros Zeros
Количество компаний с аналогичным директором в том же регионе has 80639 (16.4%) zeros Zeros
Участие в госконтрактах (количество) has 8239 (1.7%) zeros Zeros

Reproduction

Analysis started2025-05-24 13:25:05.858830
Analysis finished2025-05-24 13:26:23.686296
Duration1 minute and 17.83 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

17 - Юр. лица, имеющие задолженность по уплате налогов
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing298459
Missing (%)60.7%
Memory size3.8 MiB
Нет
193202 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters579606
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНет
2nd rowНет
3rd rowНет
4th rowНет
5th rowНет

Common Values

ValueCountFrequency (%)
Нет 193202
39.3%
(Missing) 298459
60.7%

Length

2025-05-24T16:26:23.738739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:23.816867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
нет 193202
100.0%

Most occurring characters

ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

17 - Юр. лица, не предоставляющие налоговую отчетность более года
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing298459
Missing (%)60.7%
Memory size3.8 MiB
Нет
193202 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters579606
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНет
2nd rowНет
3rd rowНет
4th rowНет
5th rowНет

Common Values

ValueCountFrequency (%)
Нет 193202
39.3%
(Missing) 298459
60.7%

Length

2025-05-24T16:26:23.873167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:23.919660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
нет 193202
100.0%

Most occurring characters

ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

2 - В учредителях/участниках/ акционерах клиента участие государства более 50
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing295964
Missing (%)60.2%
Memory size3.8 MiB
Нет
195679 
Да
 
18

Length

Max length3
Median length3
Mean length2.999908
Min length2

Characters and Unicode

Total characters587073
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНет
2nd rowНет
3rd rowНет
4th rowНет
5th rowНет

Common Values

ValueCountFrequency (%)
Нет 195679
39.8%
Да 18
 
< 0.1%
(Missing) 295964
60.2%

Length

2025-05-24T16:26:23.970075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:24.019412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
нет 195679
> 99.9%
да 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
Н 195679
33.3%
е 195679
33.3%
т 195679
33.3%
Д 18
 
< 0.1%
а 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 587073
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Н 195679
33.3%
е 195679
33.3%
т 195679
33.3%
Д 18
 
< 0.1%
а 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 587073
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Н 195679
33.3%
е 195679
33.3%
т 195679
33.3%
Д 18
 
< 0.1%
а 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 587073
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Н 195679
33.3%
е 195679
33.3%
т 195679
33.3%
Д 18
 
< 0.1%
а 18
 
< 0.1%

25 - Государственные заказы и контракты Количество заключенных контрактов
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct223
Distinct (%)1.0%
Missing468273
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean6.8113135
Minimum0
Maximum2613
Zeros7653
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:24.077883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile21
Maximum2613
Range2613
Interquartile range (IQR)3

Descriptive statistics

Standard deviation47.213634
Coefficient of variation (CV)6.9316489
Kurtosis1411.2783
Mean6.8113135
Median Absolute Deviation (MAD)1
Skewness31.820391
Sum159303
Variance2229.1272
MonotonicityNot monotonic
2025-05-24T16:26:24.150261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7653
 
1.6%
1 6229
 
1.3%
2 2697
 
0.5%
3 1373
 
0.3%
4 891
 
0.2%
5 738
 
0.2%
6 543
 
0.1%
7 339
 
0.1%
8 288
 
0.1%
10 199
 
< 0.1%
Other values (213) 2438
 
0.5%
(Missing) 468273
95.2%
ValueCountFrequency (%)
0 7653
1.6%
1 6229
1.3%
2 2697
 
0.5%
3 1373
 
0.3%
4 891
 
0.2%
5 738
 
0.2%
6 543
 
0.1%
7 339
 
0.1%
8 288
 
0.1%
9 189
 
< 0.1%
ValueCountFrequency (%)
2613 2
< 0.1%
2441 1
< 0.1%
1871 1
< 0.1%
1849 1
< 0.1%
1186 1
< 0.1%
1149 1
< 0.1%
1144 1
< 0.1%
1070 1
< 0.1%
1044 1
< 0.1%
1026 1
< 0.1%

25 - Государственные заказы и контракты Приняла участие (количество)
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct326
Distinct (%)1.4%
Missing468273
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean12.292757
Minimum0
Maximum2758
Zeros1342
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:24.216662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q37
95-th percentile42
Maximum2758
Range2758
Interquartile range (IQR)6

Descriptive statistics

Standard deviation62.053213
Coefficient of variation (CV)5.0479492
Kurtosis681.69618
Mean12.292757
Median Absolute Deviation (MAD)1
Skewness21.626233
Sum287503
Variance3850.6012
MonotonicityNot monotonic
2025-05-24T16:26:24.283582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8110
 
1.6%
2 3181
 
0.6%
3 1586
 
0.3%
0 1342
 
0.3%
4 1332
 
0.3%
5 837
 
0.2%
9 784
 
0.2%
6 657
 
0.1%
8 555
 
0.1%
7 504
 
0.1%
Other values (316) 4500
 
0.9%
(Missing) 468273
95.2%
ValueCountFrequency (%)
0 1342
 
0.3%
1 8110
1.6%
2 3181
 
0.6%
3 1586
 
0.3%
4 1332
 
0.3%
5 837
 
0.2%
6 657
 
0.1%
7 504
 
0.1%
8 555
 
0.1%
9 784
 
0.2%
ValueCountFrequency (%)
2758 1
< 0.1%
2721 1
< 0.1%
2243 1
< 0.1%
2179 1
< 0.1%
2096 2
< 0.1%
1625 1
< 0.1%
1618 1
< 0.1%
1358 1
< 0.1%
1329 1
< 0.1%
1290 1
< 0.1%

25 - Коммерческие заказы и контракты Количество заключенных контрактов
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct57
Distinct (%)0.3%
Missing472114
Missing (%)96.0%
Infinite0
Infinite (%)0.0%
Mean1.6190208
Minimum0
Maximum147
Zeros8239
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:24.350572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum147
Range147
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.8039267
Coefficient of variation (CV)2.349523
Kurtosis238.81704
Mean1.6190208
Median Absolute Deviation (MAD)1
Skewness10.981913
Sum31647
Variance14.469858
MonotonicityNot monotonic
2025-05-24T16:26:24.424762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8239
 
1.7%
1 6131
 
1.2%
2 2118
 
0.4%
3 951
 
0.2%
4 458
 
0.1%
7 438
 
0.1%
5 270
 
0.1%
6 246
 
0.1%
8 122
 
< 0.1%
9 120
 
< 0.1%
Other values (47) 454
 
0.1%
(Missing) 472114
96.0%
ValueCountFrequency (%)
0 8239
1.7%
1 6131
1.2%
2 2118
 
0.4%
3 951
 
0.2%
4 458
 
0.1%
5 270
 
0.1%
6 246
 
0.1%
7 438
 
0.1%
8 122
 
< 0.1%
9 120
 
< 0.1%
ValueCountFrequency (%)
147 1
< 0.1%
109 1
< 0.1%
93 2
< 0.1%
88 1
< 0.1%
73 1
< 0.1%
70 1
< 0.1%
66 1
< 0.1%
59 1
< 0.1%
57 1
< 0.1%
55 2
< 0.1%

25 - Коммерческие заказы и контракты Приняла участие (количество)
Real number (ℝ)

High correlation  Missing 

Distinct114
Distinct (%)0.6%
Missing472114
Missing (%)96.0%
Infinite0
Infinite (%)0.0%
Mean4.3119149
Minimum0
Maximum462
Zeros1807
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:24.497986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile14
Maximum462
Range462
Interquartile range (IQR)2

Descriptive statistics

Standard deviation13.845413
Coefficient of variation (CV)3.2109661
Kurtosis282.07243
Mean4.3119149
Median Absolute Deviation (MAD)1
Skewness13.98195
Sum84285
Variance191.69545
MonotonicityNot monotonic
2025-05-24T16:26:24.570470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8803
 
1.8%
2 3019
 
0.6%
0 1807
 
0.4%
3 1421
 
0.3%
4 922
 
0.2%
5 647
 
0.1%
6 404
 
0.1%
7 331
 
0.1%
9 297
 
0.1%
10 238
 
< 0.1%
Other values (104) 1658
 
0.3%
(Missing) 472114
96.0%
ValueCountFrequency (%)
0 1807
 
0.4%
1 8803
1.8%
2 3019
 
0.6%
3 1421
 
0.3%
4 922
 
0.2%
5 647
 
0.1%
6 404
 
0.1%
7 331
 
0.1%
8 223
 
< 0.1%
9 297
 
0.1%
ValueCountFrequency (%)
462 1
 
< 0.1%
419 1
 
< 0.1%
405 1
 
< 0.1%
345 1
 
< 0.1%
252 7
< 0.1%
238 14
< 0.1%
232 1
 
< 0.1%
221 1
 
< 0.1%
212 1
 
< 0.1%
208 1
 
< 0.1%

4 - Перечень ОАО по Распоряжению Правительства № 91-Р
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing298459
Missing (%)60.7%
Memory size3.8 MiB
Нет
193202 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters579606
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНет
2nd rowНет
3rd rowНет
4th rowНет
5th rowНет

Common Values

ValueCountFrequency (%)
Нет 193202
39.3%
(Missing) 298459
60.7%

Length

2025-05-24T16:26:24.634114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:24.680539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
нет 193202
100.0%

Most occurring characters

ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

4 - Перечень ФГУП, имеющих существенное значение
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing298459
Missing (%)60.7%
Memory size3.8 MiB
Нет
193202 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters579606
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНет
2nd rowНет
3rd rowНет
4th rowНет
5th rowНет

Common Values

ValueCountFrequency (%)
Нет 193202
39.3%
(Missing) 298459
60.7%

Length

2025-05-24T16:26:24.730212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:24.857058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
нет 193202
100.0%

Most occurring characters

ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

4 - Реестр оборонно-промышленного комплекса
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing298459
Missing (%)60.7%
Memory size3.8 MiB
Нет
193202 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters579606
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНет
2nd rowНет
3rd rowНет
4th rowНет
5th rowНет

Common Values

ValueCountFrequency (%)
Нет 193202
39.3%
(Missing) 298459
60.7%

Length

2025-05-24T16:26:24.906804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:24.955039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
нет 193202
100.0%

Most occurring characters

ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

4 - Реестр операторов, осуществляющих обработку персональных данных
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing298459
Missing (%)60.7%
Memory size3.8 MiB
Нет
193202 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters579606
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНет
2nd rowНет
3rd rowНет
4th rowНет
5th rowНет

Common Values

ValueCountFrequency (%)
Нет 193202
39.3%
(Missing) 298459
60.7%

Length

2025-05-24T16:26:25.004993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:25.051922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
нет 193202
100.0%

Most occurring characters

ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

6 - Организация, первое лицо, учредители входит в список «дисквалифицированных»
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing295964
Missing (%)60.2%
Memory size3.8 MiB
Нет
195421 
Да
 
276

Length

Max length3
Median length3
Mean length2.9985897
Min length2

Characters and Unicode

Total characters586815
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНет
2nd rowНет
3rd rowНет
4th rowНет
5th rowНет

Common Values

ValueCountFrequency (%)
Нет 195421
39.7%
Да 276
 
0.1%
(Missing) 295964
60.2%

Length

2025-05-24T16:26:25.103608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:25.154518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
нет 195421
99.9%
да 276
 
0.1%

Most occurring characters

ValueCountFrequency (%)
Н 195421
33.3%
е 195421
33.3%
т 195421
33.3%
Д 276
 
< 0.1%
а 276
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Н 195421
33.3%
е 195421
33.3%
т 195421
33.3%
Д 276
 
< 0.1%
а 276
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Н 195421
33.3%
е 195421
33.3%
т 195421
33.3%
Д 276
 
< 0.1%
а 276
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Н 195421
33.3%
е 195421
33.3%
т 195421
33.3%
Д 276
 
< 0.1%
а 276
 
< 0.1%

7 - массовый контактный телефон: в заявке указан телефон, на который зарегистрировано более Х компаний (кроме компаний-агентов)
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct152
Distinct (%)0.1%
Missing379036
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean1.6076626
Minimum0
Maximum799
Zeros66623
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:25.213399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum799
Range799
Interquartile range (IQR)1

Descriptive statistics

Standard deviation11.83747
Coefficient of variation (CV)7.3631558
Kurtosis1311.6445
Mean1.6076626
Median Absolute Deviation (MAD)0
Skewness31.377407
Sum181063
Variance140.1257
MonotonicityNot monotonic
2025-05-24T16:26:25.291503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66623
 
13.6%
1 22088
 
4.5%
2 11384
 
2.3%
3 4671
 
1.0%
4 2363
 
0.5%
5 1345
 
0.3%
6 752
 
0.2%
7 489
 
0.1%
8 362
 
0.1%
9 357
 
0.1%
Other values (142) 2191
 
0.4%
(Missing) 379036
77.1%
ValueCountFrequency (%)
0 66623
13.6%
1 22088
 
4.5%
2 11384
 
2.3%
3 4671
 
1.0%
4 2363
 
0.5%
5 1345
 
0.3%
6 752
 
0.2%
7 489
 
0.1%
8 362
 
0.1%
9 357
 
0.1%
ValueCountFrequency (%)
799 1
 
< 0.1%
797 2
< 0.1%
555 1
 
< 0.1%
544 3
< 0.1%
534 1
 
< 0.1%
495 3
< 0.1%
485 1
 
< 0.1%
484 1
 
< 0.1%
477 1
 
< 0.1%
474 1
 
< 0.1%

8 - Количество компаний с аналогичным директором
Real number (ℝ)

High correlation  Missing 

Distinct170
Distinct (%)0.2%
Missing379036
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean4.0744151
Minimum0
Maximum266
Zeros484
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:25.367300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile15
Maximum266
Range266
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.098942
Coefficient of variation (CV)2.4786237
Kurtosis137.2796
Mean4.0744151
Median Absolute Deviation (MAD)1
Skewness9.5839276
Sum458881
Variance101.98863
MonotonicityNot monotonic
2025-05-24T16:26:25.441347image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 52872
 
10.8%
2 23690
 
4.8%
3 10285
 
2.1%
4 6539
 
1.3%
5 3545
 
0.7%
6 2259
 
0.5%
7 1661
 
0.3%
8 1272
 
0.3%
9 1050
 
0.2%
10 912
 
0.2%
Other values (160) 8540
 
1.7%
(Missing) 379036
77.1%
ValueCountFrequency (%)
0 484
 
0.1%
1 52872
10.8%
2 23690
4.8%
3 10285
 
2.1%
4 6539
 
1.3%
5 3545
 
0.7%
6 2259
 
0.5%
7 1661
 
0.3%
8 1272
 
0.3%
9 1050
 
0.2%
ValueCountFrequency (%)
266 1
 
< 0.1%
256 1
 
< 0.1%
251 1
 
< 0.1%
249 1
 
< 0.1%
247 1
 
< 0.1%
245 9
< 0.1%
243 1
 
< 0.1%
241 1
 
< 0.1%
223 1
 
< 0.1%
222 1
 
< 0.1%

8 - Количество компаний с аналогичным директором в том же регионе
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct45
Distinct (%)< 0.1%
Missing298883
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean0.99323056
Minimum0
Maximum75
Zeros80639
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:25.509016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum75
Range75
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7910392
Coefficient of variation (CV)1.8032461
Kurtosis352.00526
Mean0.99323056
Median Absolute Deviation (MAD)1
Skewness13.683816
Sum191473
Variance3.2078213
MonotonicityNot monotonic
2025-05-24T16:26:25.580829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 80639
 
16.4%
1 72430
 
14.7%
2 24528
 
5.0%
3 7889
 
1.6%
4 3581
 
0.7%
5 1439
 
0.3%
6 708
 
0.1%
7 389
 
0.1%
8 292
 
0.1%
9 168
 
< 0.1%
Other values (35) 715
 
0.1%
(Missing) 298883
60.8%
ValueCountFrequency (%)
0 80639
16.4%
1 72430
14.7%
2 24528
 
5.0%
3 7889
 
1.6%
4 3581
 
0.7%
5 1439
 
0.3%
6 708
 
0.1%
7 389
 
0.1%
8 292
 
0.1%
9 168
 
< 0.1%
ValueCountFrequency (%)
75 1
 
< 0.1%
57 57
< 0.1%
48 2
 
< 0.1%
41 2
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
37 2
 
< 0.1%
36 4
 
< 0.1%
35 4
 
< 0.1%

IdInquiry
Real number (ℝ)

High correlation 

Distinct387117
Distinct (%)78.8%
Missing126
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean386457.94
Minimum149105
Maximum622694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:25.654706image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum149105
5-th percentile179509.4
Q1277315
median380943
Q3499951
95-th percentile597489.3
Maximum622694
Range473589
Interquartile range (IQR)222636

Descriptive statistics

Standard deviation132195.07
Coefficient of variation (CV)0.34206844
Kurtosis-1.1485508
Mean386457.94
Median Absolute Deviation (MAD)110167
Skewness0.054980081
Sum1.899576 × 1011
Variance1.7475536 × 1010
MonotonicityNot monotonic
2025-05-24T16:26:25.728014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
268336 396
 
0.1%
270982 220
 
< 0.1%
186394 210
 
< 0.1%
314326 96
 
< 0.1%
258529 88
 
< 0.1%
347336 88
 
< 0.1%
421319 60
 
< 0.1%
421333 60
 
< 0.1%
421291 60
 
< 0.1%
421251 60
 
< 0.1%
Other values (387107) 490197
99.7%
(Missing) 126
 
< 0.1%
ValueCountFrequency (%)
149105 1
< 0.1%
149106 2
< 0.1%
149107 2
< 0.1%
149109 1
< 0.1%
149110 2
< 0.1%
149112 1
< 0.1%
149113 1
< 0.1%
149114 1
< 0.1%
149115 1
< 0.1%
149116 1
< 0.1%
ValueCountFrequency (%)
622694 1
< 0.1%
622693 1
< 0.1%
622692 1
< 0.1%
622691 1
< 0.1%
622690 1
< 0.1%
622689 1
< 0.1%
622688 1
< 0.1%
622687 1
< 0.1%
622686 1
< 0.1%
622685 1
< 0.1%

Month
Unsupported

Rejected  Unsupported 

Missing126
Missing (%)< 0.1%
Memory size3.8 MiB

has_products
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing126
Missing (%)< 0.1%
Memory size3.8 MiB
False
471686 
True
 
19849
(Missing)
 
126
ValueCountFrequency (%)
False 471686
95.9%
True 19849
 
4.0%
(Missing) 126
 
< 0.1%
2025-05-24T16:26:25.782855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

hour
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing126
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.322105
Minimum0
Maximum23
Zeros72
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:25.829200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median12
Q315
95-th percentile17
Maximum23
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3611842
Coefficient of variation (CV)0.27277678
Kurtosis-0.1466879
Mean12.322105
Median Absolute Deviation (MAD)2
Skewness-0.33834605
Sum6056746
Variance11.297559
MonotonicityNot monotonic
2025-05-24T16:26:25.887384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11 54055
11.0%
12 52531
10.7%
10 50469
10.3%
15 48393
9.8%
14 48175
9.8%
13 47933
9.7%
16 42127
8.6%
9 37023
7.5%
17 32031
6.5%
8 19416
 
3.9%
Other values (14) 59382
12.1%
ValueCountFrequency (%)
0 72
 
< 0.1%
1 447
 
0.1%
2 1331
 
0.3%
3 2615
 
0.5%
4 4104
 
0.8%
5 7414
 
1.5%
6 9037
 
1.8%
7 13491
 
2.7%
8 19416
3.9%
9 37023
7.5%
ValueCountFrequency (%)
23 10
 
< 0.1%
22 17
 
< 0.1%
21 152
 
< 0.1%
20 1114
 
0.2%
19 4932
 
1.0%
18 14646
 
3.0%
17 32031
6.5%
16 42127
8.6%
15 48393
9.8%
14 48175
9.8%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing126
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.5296978
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:25.941749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0847713
Coefficient of variation (CV)0.40968063
Kurtosis-0.97699025
Mean7.5296978
Median Absolute Deviation (MAD)2
Skewness-0.37341356
Sum3701110
Variance9.5158142
MonotonicityNot monotonic
2025-05-24T16:26:25.997060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 63773
13.0%
11 61883
12.6%
9 57198
11.6%
8 51218
10.4%
7 46983
9.6%
4 41742
8.5%
12 37621
7.7%
3 37126
7.6%
6 35918
7.3%
5 26591
5.4%
Other values (2) 31482
6.4%
ValueCountFrequency (%)
1 11965
 
2.4%
2 19517
 
4.0%
3 37126
7.6%
4 41742
8.5%
5 26591
5.4%
6 35918
7.3%
7 46983
9.6%
8 51218
10.4%
9 57198
11.6%
10 63773
13.0%
ValueCountFrequency (%)
12 37621
7.7%
11 61883
12.6%
10 63773
13.0%
9 57198
11.6%
8 51218
10.4%
7 46983
9.6%
6 35918
7.3%
5 26591
5.4%
4 41742
8.5%
3 37126
7.6%

target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing126
Missing (%)< 0.1%
Memory size3.8 MiB
0.0
433590 
1.0
57945 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1474605
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 433590
88.2%
1.0 57945
 
11.8%
(Missing) 126
 
< 0.1%

Length

2025-05-24T16:26:26.055797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:26.100532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 433590
88.2%
1.0 57945
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 925125
62.7%
. 491535
33.3%
1 57945
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1474605
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 925125
62.7%
. 491535
33.3%
1 57945
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1474605
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 925125
62.7%
. 491535
33.3%
1 57945
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1474605
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 925125
62.7%
. 491535
33.3%
1 57945
 
3.9%

weekday
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing126
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.0003621
Minimum0
Maximum6
Zeros94078
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:26.141690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3953226
Coefficient of variation (CV)0.69753501
Kurtosis-1.2186404
Mean2.0003621
Median Absolute Deviation (MAD)1
Skewness0.016928412
Sum983248
Variance1.9469252
MonotonicityNot monotonic
2025-05-24T16:26:26.192731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 103768
21.1%
2 101755
20.7%
1 100455
20.4%
0 94078
19.1%
4 89523
18.2%
5 1849
 
0.4%
6 107
 
< 0.1%
(Missing) 126
 
< 0.1%
ValueCountFrequency (%)
0 94078
19.1%
1 100455
20.4%
2 101755
20.7%
3 103768
21.1%
4 89523
18.2%
5 1849
 
0.4%
6 107
 
< 0.1%
ValueCountFrequency (%)
6 107
 
< 0.1%
5 1849
 
0.4%
4 89523
18.2%
3 103768
21.1%
2 101755
20.7%
1 100455
20.4%
0 94078
19.1%

Выручка компании (млн, руб)
Text

Missing 

Distinct2850
Distinct (%)5.4%
Missing438555
Missing (%)89.2%
Memory size3.8 MiB
2025-05-24T16:26:26.357039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.2649606
Min length1

Characters and Unicode

Total characters173389
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1087 ?
Unique (%)2.0%

Sample

1st row5
2nd row0
3rd row0
4th row9,10
5th row6,10
ValueCountFrequency (%)
0 19170
36.1%
121,70 839
 
1.6%
0,10 564
 
1.1%
0,20 414
 
0.8%
0,40 361
 
0.7%
0,30 361
 
0.7%
0,50 291
 
0.5%
1,40 267
 
0.5%
0,60 263
 
0.5%
0,90 249
 
0.5%
Other values (2839) 30327
57.1%
2025-05-24T16:26:26.593654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 55738
32.1%
, 30784
17.8%
1 17520
 
10.1%
2 12648
 
7.3%
3 9536
 
5.5%
4 9033
 
5.2%
5 8259
 
4.8%
7 8203
 
4.7%
6 7786
 
4.5%
9 6961
 
4.0%
Other values (2) 6921
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 173389
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 55738
32.1%
, 30784
17.8%
1 17520
 
10.1%
2 12648
 
7.3%
3 9536
 
5.5%
4 9033
 
5.2%
5 8259
 
4.8%
7 8203
 
4.7%
6 7786
 
4.5%
9 6961
 
4.0%
Other values (2) 6921
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 173389
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 55738
32.1%
, 30784
17.8%
1 17520
 
10.1%
2 12648
 
7.3%
3 9536
 
5.5%
4 9033
 
5.2%
5 8259
 
4.8%
7 8203
 
4.7%
6 7786
 
4.5%
9 6961
 
4.0%
Other values (2) 6921
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 173389
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 55738
32.1%
, 30784
17.8%
1 17520
 
10.1%
2 12648
 
7.3%
3 9536
 
5.5%
4 9033
 
5.2%
5 8259
 
4.8%
7 8203
 
4.7%
6 7786
 
4.5%
9 6961
 
4.0%
Other values (2) 6921
 
4.0%

Дата блокировки
Date

Missing 

Distinct397
Distinct (%)0.6%
Missing427115
Missing (%)86.9%
Memory size3.8 MiB
Minimum2018-06-18 00:00:00
Maximum2020-02-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-24T16:26:26.665434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:26.740260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Дата закрытия
Date

Missing 

Distinct412
Distinct (%)0.4%
Missing374958
Missing (%)76.3%
Memory size3.8 MiB
Minimum2018-06-28 00:00:00
Maximum2020-02-21 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-24T16:26:26.811104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:26.886633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Дата заявки
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.8 MiB

Дата заявки_report
Date

Missing 

Distinct138166
Distinct (%)70.6%
Missing296090
Missing (%)60.2%
Memory size3.8 MiB
Minimum2018-06-18 03:34:03.993000
Maximum2019-12-13 17:55:48.830000
Invalid dates0
Invalid dates (%)0.0%
2025-05-24T16:26:26.957367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:27.033191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Дата открытия
Date

Missing 

Distinct402
Distinct (%)0.1%
Missing84344
Missing (%)17.2%
Memory size3.8 MiB
Minimum2018-06-18 00:00:00
Maximum2019-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-24T16:26:27.104820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:27.181650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Дата регистрации
Date

Missing 

Distinct5792
Distinct (%)3.0%
Missing298505
Missing (%)60.7%
Memory size3.8 MiB
Minimum1964-10-13 00:00:00
Maximum2019-12-09 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-24T16:26:27.252809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:27.331758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Даты внесения соучредителей
Text

Missing 

Distinct15183
Distinct (%)13.6%
Missing379758
Missing (%)77.2%
Memory size3.8 MiB
2025-05-24T16:26:27.517921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length241
Median length10
Mean length15.509289
Min length10

Characters and Unicode

Total characters1735536
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8349 ?
Unique (%)7.5%

Sample

1st row26.04.2013
2nd row11.09.2018
3rd row11.09.2018
4th row16.06.2014
5th row22.09.2011
ValueCountFrequency (%)
26.07.2011;21.06.2011;12.10.2009 836
 
0.7%
19.03.2019 558
 
0.5%
22.03.2019 544
 
0.5%
26.02.2019 539
 
0.5%
28.01.2019 526
 
0.5%
11.02.2019 511
 
0.5%
06.02.2019 506
 
0.5%
26.03.2019 502
 
0.4%
06.03.2019 480
 
0.4%
03.04.2019 478
 
0.4%
Other values (15173) 106423
95.1%
2025-05-24T16:26:27.764139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 392598
22.6%
. 335898
19.4%
1 304819
17.6%
2 288408
16.6%
8 76210
 
4.4%
9 67319
 
3.9%
; 56046
 
3.2%
3 50047
 
2.9%
7 45212
 
2.6%
6 41670
 
2.4%
Other values (2) 77309
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1735536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 392598
22.6%
. 335898
19.4%
1 304819
17.6%
2 288408
16.6%
8 76210
 
4.4%
9 67319
 
3.9%
; 56046
 
3.2%
3 50047
 
2.9%
7 45212
 
2.6%
6 41670
 
2.4%
Other values (2) 77309
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1735536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 392598
22.6%
. 335898
19.4%
1 304819
17.6%
2 288408
16.6%
8 76210
 
4.4%
9 67319
 
3.9%
; 56046
 
3.2%
3 50047
 
2.9%
7 45212
 
2.6%
6 41670
 
2.4%
Other values (2) 77309
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1735536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 392598
22.6%
. 335898
19.4%
1 304819
17.6%
2 288408
16.6%
8 76210
 
4.4%
9 67319
 
3.9%
; 56046
 
3.2%
3 50047
 
2.9%
7 45212
 
2.6%
6 41670
 
2.4%
Other values (2) 77309
 
4.5%

Заявки до даты
Real number (ℝ)

Zeros 

Distinct39
Distinct (%)< 0.1%
Missing126
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.57607902
Minimum0
Maximum38
Zeros290974
Zeros (%)59.2%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:27.830925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum38
Range38
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0811141
Coefficient of variation (CV)1.8766768
Kurtosis179.80317
Mean0.57607902
Median Absolute Deviation (MAD)0
Skewness8.8868318
Sum283163
Variance1.1688077
MonotonicityNot monotonic
2025-05-24T16:26:27.898834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 290974
59.2%
1 152631
31.0%
2 33985
 
6.9%
3 8366
 
1.7%
4 2427
 
0.5%
5 923
 
0.2%
6 493
 
0.1%
7 343
 
0.1%
8 280
 
0.1%
9 248
 
0.1%
Other values (29) 865
 
0.2%
ValueCountFrequency (%)
0 290974
59.2%
1 152631
31.0%
2 33985
 
6.9%
3 8366
 
1.7%
4 2427
 
0.5%
5 923
 
0.2%
6 493
 
0.1%
7 343
 
0.1%
8 280
 
0.1%
9 248
 
0.1%
ValueCountFrequency (%)
38 6
< 0.1%
37 6
< 0.1%
36 6
< 0.1%
35 6
< 0.1%
34 6
< 0.1%
33 6
< 0.1%
32 6
< 0.1%
31 6
< 0.1%
30 6
< 0.1%
29 6
< 0.1%

ИНН
Text

Distinct243851
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:28.081316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.96451
Min length10

Characters and Unicode

Total characters5390822
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118111 ?
Unique (%)24.0%

Sample

1st rowAAAAIRYZRC
2nd rowAAAAIRYZRC
3rd rowAAADCVZNUR
4th rowAAAFBXICUBOO
5th rowAAAFBXICUBOO
ValueCountFrequency (%)
aglsebfwdv 836
 
0.2%
nmohwohlmy 606
 
0.1%
grreypdknoax 400
 
0.1%
zorezqqwxtja 362
 
0.1%
zcklsllkpw 234
 
< 0.1%
smceujwfxxdd 229
 
< 0.1%
irgktdvovo 225
 
< 0.1%
qwzgvjaqvx 180
 
< 0.1%
nuyshdqndh 144
 
< 0.1%
jairnuqyxa 144
 
< 0.1%
Other values (243841) 488301
99.3%
2025-05-24T16:26:28.329125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 209277
 
3.9%
O 209116
 
3.9%
B 208881
 
3.9%
L 208858
 
3.9%
M 208622
 
3.9%
X 208275
 
3.9%
E 208145
 
3.9%
D 208078
 
3.9%
K 207824
 
3.9%
P 207453
 
3.8%
Other values (16) 3306293
61.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5390822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 209277
 
3.9%
O 209116
 
3.9%
B 208881
 
3.9%
L 208858
 
3.9%
M 208622
 
3.9%
X 208275
 
3.9%
E 208145
 
3.9%
D 208078
 
3.9%
K 207824
 
3.9%
P 207453
 
3.8%
Other values (16) 3306293
61.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5390822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 209277
 
3.9%
O 209116
 
3.9%
B 208881
 
3.9%
L 208858
 
3.9%
M 208622
 
3.9%
X 208275
 
3.9%
E 208145
 
3.9%
D 208078
 
3.9%
K 207824
 
3.9%
P 207453
 
3.8%
Other values (16) 3306293
61.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5390822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 209277
 
3.9%
O 209116
 
3.9%
B 208881
 
3.9%
L 208858
 
3.9%
M 208622
 
3.9%
X 208275
 
3.9%
E 208145
 
3.9%
D 208078
 
3.9%
K 207824
 
3.9%
P 207453
 
3.8%
Other values (16) 3306293
61.3%

Индекс должной осмотрительности
Real number (ℝ)

High correlation  Missing 

Distinct98
Distinct (%)0.1%
Missing381160
Missing (%)77.5%
Infinite0
Infinite (%)0.0%
Mean41.099791
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:28.400977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q126
median43
Q354
95-th percentile76
Maximum98
Range97
Interquartile range (IQR)28

Descriptive statistics

Standard deviation19.627682
Coefficient of variation (CV)0.47756161
Kurtosis-0.14384193
Mean41.099791
Median Absolute Deviation (MAD)14
Skewness0.22640862
Sum4541568
Variance385.24591
MonotonicityNot monotonic
2025-05-24T16:26:28.472336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 4668
 
0.9%
55 4226
 
0.9%
25 3678
 
0.7%
26 3573
 
0.7%
27 3496
 
0.7%
46 3399
 
0.7%
28 3287
 
0.7%
29 3025
 
0.6%
57 2882
 
0.6%
52 2794
 
0.6%
Other values (88) 75473
 
15.4%
(Missing) 381160
77.5%
ValueCountFrequency (%)
1 9
 
< 0.1%
2 534
0.1%
3 1138
0.2%
4 871
0.2%
5 819
0.2%
6 833
0.2%
7 728
0.1%
8 738
0.2%
9 697
0.1%
10 744
0.2%
ValueCountFrequency (%)
98 195
< 0.1%
97 244
< 0.1%
96 182
< 0.1%
95 174
< 0.1%
94 151
< 0.1%
93 194
< 0.1%
92 201
< 0.1%
91 244
< 0.1%
90 228
< 0.1%
89 273
0.1%

Индекс должной осмотрительности описание
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing378993
Missing (%)77.1%
Memory size3.8 MiB
Средний риск
96972 
Низкий риск
10782 
Высокий риск
 
2747
Недостаточно данных для расчета индекса финансового риска
 
2167

Length

Max length57
Median length12
Mean length12.76981
Min length11

Characters and Unicode

Total characters1438749
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНизкий риск
2nd rowСредний риск
3rd rowСредний риск
4th rowСредний риск
5th rowСредний риск

Common Values

ValueCountFrequency (%)
Средний риск 96972
 
19.7%
Низкий риск 10782
 
2.2%
Высокий риск 2747
 
0.6%
Недостаточно данных для расчета индекса финансового риска 2167
 
0.4%
(Missing) 378993
77.1%

Length

2025-05-24T16:26:28.535270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:28.585205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
риск 110501
46.8%
средний 96972
41.1%
низкий 10782
 
4.6%
высокий 2747
 
1.2%
недостаточно 2167
 
0.9%
данных 2167
 
0.9%
для 2167
 
0.9%
расчета 2167
 
0.9%
индекса 2167
 
0.9%
финансового 2167
 
0.9%

Most occurring characters

ValueCountFrequency (%)
и 238285
16.6%
р 211807
14.7%
к 128364
8.9%
с 124083
8.6%
123503
8.6%
й 110501
7.7%
н 109974
7.6%
д 105640
7.3%
е 103473
7.2%
С 96972
6.7%
Other values (14) 86147
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1438749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
и 238285
16.6%
р 211807
14.7%
к 128364
8.9%
с 124083
8.6%
123503
8.6%
й 110501
7.7%
н 109974
7.6%
д 105640
7.3%
е 103473
7.2%
С 96972
6.7%
Other values (14) 86147
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1438749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
и 238285
16.6%
р 211807
14.7%
к 128364
8.9%
с 124083
8.6%
123503
8.6%
й 110501
7.7%
н 109974
7.6%
д 105640
7.3%
е 103473
7.2%
С 96972
6.7%
Other values (14) 86147
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1438749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
и 238285
16.6%
р 211807
14.7%
к 128364
8.9%
с 124083
8.6%
123503
8.6%
й 110501
7.7%
н 109974
7.6%
д 105640
7.3%
е 103473
7.2%
С 96972
6.7%
Other values (14) 86147
 
6.0%

Индекс финансового риска
Real number (ℝ)

High correlation  Missing 

Distinct98
Distinct (%)0.1%
Missing385774
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean37.787368
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:28.651394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q118
median39
Q353
95-th percentile70
Maximum98
Range97
Interquartile range (IQR)35

Descriptive statistics

Standard deviation22.743736
Coefficient of variation (CV)0.60188728
Kurtosis-1.0319506
Mean37.787368
Median Absolute Deviation (MAD)18
Skewness-0.16042077
Sum4001191
Variance517.27752
MonotonicityNot monotonic
2025-05-24T16:26:28.723943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 13051
 
2.7%
50 10408
 
2.1%
52 6997
 
1.4%
36 6490
 
1.3%
1 5496
 
1.1%
39 5148
 
1.0%
22 4351
 
0.9%
2 3600
 
0.7%
48 2389
 
0.5%
3 2243
 
0.5%
Other values (88) 45714
 
9.3%
(Missing) 385774
78.5%
ValueCountFrequency (%)
1 5496
1.1%
2 3600
0.7%
3 2243
0.5%
4 1854
 
0.4%
5 1987
 
0.4%
6 1182
 
0.2%
7 1243
 
0.3%
8 898
 
0.2%
9 853
 
0.2%
10 1216
 
0.2%
ValueCountFrequency (%)
98 4
 
< 0.1%
97 13
 
< 0.1%
96 24
 
< 0.1%
95 24
 
< 0.1%
94 41
< 0.1%
93 43
< 0.1%
92 82
< 0.1%
91 86
< 0.1%
90 63
< 0.1%
89 95
< 0.1%

Индекс финансового риска описание
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing378993
Missing (%)77.1%
Memory size3.8 MiB
Низкий риск
53411 
Средний риск
47313 
Расчет индекса не осуществляется для следующих категорий юридических лиц: Унитарные предприятия, находящиеся в федеральной или муниципальной собственности; Банки; Страховые компании; Некоммерческие организации; Организации без прав юридического лица.
6781 
Высокий риск
 
5163

Length

Max length250
Median length12
Mean length25.850135
Min length11

Characters and Unicode

Total characters2912483
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНизкий риск
2nd rowСредний риск
3rd rowСредний риск
4th rowНизкий риск
5th rowВысокий риск

Common Values

ValueCountFrequency (%)
Низкий риск 53411
 
10.9%
Средний риск 47313
 
9.6%
Расчет индекса не осуществляется для следующих категорий юридических лиц: Унитарные предприятия, находящиеся в федеральной или муниципальной собственности; Банки; Страховые компании; Некоммерческие организации; Организации без прав юридического лица. 6781
 
1.4%
Высокий риск 5163
 
1.1%
(Missing) 378993
77.1%

Length

2025-05-24T16:26:28.789274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:28.842268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
риск 105887
26.8%
низкий 53411
 
13.5%
средний 47313
 
12.0%
организации 13562
 
3.4%
федеральной 6781
 
1.7%
лица 6781
 
1.7%
юридического 6781
 
1.7%
прав 6781
 
1.7%
без 6781
 
1.7%
некоммерческие 6781
 
1.7%
Other values (20) 134002
33.9%

Most occurring characters

ValueCountFrequency (%)
и 461834
15.9%
282193
 
9.7%
р 234572
 
8.1%
к 218709
 
7.5%
с 199203
 
6.8%
е 189714
 
6.5%
н 142247
 
4.9%
й 126230
 
4.3%
а 108496
 
3.7%
д 101561
 
3.5%
Other values (30) 847724
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2912483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
и 461834
15.9%
282193
 
9.7%
р 234572
 
8.1%
к 218709
 
7.5%
с 199203
 
6.8%
е 189714
 
6.5%
н 142247
 
4.9%
й 126230
 
4.3%
а 108496
 
3.7%
д 101561
 
3.5%
Other values (30) 847724
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2912483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
и 461834
15.9%
282193
 
9.7%
р 234572
 
8.1%
к 218709
 
7.5%
с 199203
 
6.8%
е 189714
 
6.5%
н 142247
 
4.9%
й 126230
 
4.3%
а 108496
 
3.7%
д 101561
 
3.5%
Other values (30) 847724
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2912483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
и 461834
15.9%
282193
 
9.7%
р 234572
 
8.1%
к 218709
 
7.5%
с 199203
 
6.8%
е 189714
 
6.5%
н 142247
 
4.9%
й 126230
 
4.3%
а 108496
 
3.7%
д 101561
 
3.5%
Other values (30) 847724
29.1%

История смены сооучредителей
Text

Missing 

Distinct15706
Distinct (%)14.0%
Missing379758
Missing (%)77.2%
Memory size3.8 MiB
2025-05-24T16:26:28.979647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1141
Median length22
Mean length34.793473
Min length22

Characters and Unicode

Total characters3893494
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8724 ?
Unique (%)7.8%

Sample

1st rowЮЛ: 26.04.2013 0:00:00
2nd rowЮЛ: 11.09.2018 0:00:00
3rd rowЮЛ: 11.09.2018 0:00:00
4th rowФЛ: 16.06.2014 0:00:00
5th rowЮЛ: 22.09.2011 0:00:00
ValueCountFrequency (%)
0:00:00 171094
33.3%
юл 162467
31.7%
фл 7623
 
1.5%
юл-нерезидент 1004
 
0.2%
12.10.2009 898
 
0.2%
26.07.2011 856
 
0.2%
21.06.2011 854
 
0.2%
26.03.2019 743
 
0.1%
22.03.2019 647
 
0.1%
19.03.2019 615
 
0.1%
Other values (4374) 166481
32.4%
2025-05-24T16:26:29.193412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1255914
32.3%
: 513282
13.2%
401379
 
10.3%
. 342188
 
8.8%
1 310074
 
8.0%
2 294160
 
7.6%
Л 171094
 
4.4%
Ю 163471
 
4.2%
8 77461
 
2.0%
9 68356
 
1.8%
Other values (15) 296115
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3893494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1255914
32.3%
: 513282
13.2%
401379
 
10.3%
. 342188
 
8.8%
1 310074
 
8.0%
2 294160
 
7.6%
Л 171094
 
4.4%
Ю 163471
 
4.2%
8 77461
 
2.0%
9 68356
 
1.8%
Other values (15) 296115
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3893494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1255914
32.3%
: 513282
13.2%
401379
 
10.3%
. 342188
 
8.8%
1 310074
 
8.0%
2 294160
 
7.6%
Л 171094
 
4.4%
Ю 163471
 
4.2%
8 77461
 
2.0%
9 68356
 
1.8%
Other values (15) 296115
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3893494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1255914
32.3%
: 513282
13.2%
401379
 
10.3%
. 342188
 
8.8%
1 310074
 
8.0%
2 294160
 
7.6%
Л 171094
 
4.4%
Ю 163471
 
4.2%
8 77461
 
2.0%
9 68356
 
1.8%
Other values (15) 296115
 
7.6%

Количество видов деятельности у Клиента
Real number (ℝ)

Missing 

Distinct276
Distinct (%)0.1%
Missing298770
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean16.091974
Minimum1
Maximum714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:29.263411image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q321
95-th percentile49
Maximum714
Range713
Interquartile range (IQR)16

Descriptive statistics

Standard deviation19.581876
Coefficient of variation (CV)1.2168722
Kurtosis93.514515
Mean16.091974
Median Absolute Deviation (MAD)7
Skewness6.1158008
Sum3103997
Variance383.44986
MonotonicityNot monotonic
2025-05-24T16:26:29.335912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13553
 
2.8%
2 11563
 
2.4%
3 10455
 
2.1%
4 10044
 
2.0%
5 9780
 
2.0%
6 9538
 
1.9%
7 8458
 
1.7%
8 8025
 
1.6%
10 7352
 
1.5%
9 7138
 
1.5%
Other values (266) 96985
 
19.7%
(Missing) 298770
60.8%
ValueCountFrequency (%)
1 13553
2.8%
2 11563
2.4%
3 10455
2.1%
4 10044
2.0%
5 9780
2.0%
6 9538
1.9%
7 8458
1.7%
8 8025
1.6%
9 7138
1.5%
10 7352
1.5%
ValueCountFrequency (%)
714 1
< 0.1%
637 1
< 0.1%
624 1
< 0.1%
617 1
< 0.1%
594 1
< 0.1%
580 1
< 0.1%
574 1
< 0.1%
541 1
< 0.1%
523 1
< 0.1%
490 1
< 0.1%

Количество компаний с аналогичным директором в том же регионе
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct45
Distinct (%)< 0.1%
Missing298883
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean0.99323056
Minimum0
Maximum75
Zeros80639
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:29.406089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum75
Range75
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7910392
Coefficient of variation (CV)1.8032461
Kurtosis352.00526
Mean0.99323056
Median Absolute Deviation (MAD)1
Skewness13.683816
Sum191473
Variance3.2078213
MonotonicityNot monotonic
2025-05-24T16:26:29.478247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 80639
 
16.4%
1 72430
 
14.7%
2 24528
 
5.0%
3 7889
 
1.6%
4 3581
 
0.7%
5 1439
 
0.3%
6 708
 
0.1%
7 389
 
0.1%
8 292
 
0.1%
9 168
 
< 0.1%
Other values (35) 715
 
0.1%
(Missing) 298883
60.8%
ValueCountFrequency (%)
0 80639
16.4%
1 72430
14.7%
2 24528
 
5.0%
3 7889
 
1.6%
4 3581
 
0.7%
5 1439
 
0.3%
6 708
 
0.1%
7 389
 
0.1%
8 292
 
0.1%
9 168
 
< 0.1%
ValueCountFrequency (%)
75 1
 
< 0.1%
57 57
< 0.1%
48 2
 
< 0.1%
41 2
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
37 2
 
< 0.1%
36 4
 
< 0.1%
35 4
 
< 0.1%

Количество компаний, зарегистрированных на адресе регистрации Организации по данным сайта ФНС
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct183
Distinct (%)0.2%
Missing379036
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean2.5281421
Minimum0
Maximum684
Zeros283
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:29.549519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum684
Range684
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.753358
Coefficient of variation (CV)3.8579153
Kurtosis1512.4514
Mean2.5281421
Median Absolute Deviation (MAD)0
Skewness33.073405
Sum284732
Variance95.127993
MonotonicityNot monotonic
2025-05-24T16:26:29.622574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 70456
 
14.3%
2 19366
 
3.9%
3 8292
 
1.7%
4 4319
 
0.9%
5 2304
 
0.5%
6 1656
 
0.3%
7 1202
 
0.2%
8 1015
 
0.2%
9 784
 
0.2%
10 415
 
0.1%
Other values (173) 2816
 
0.6%
(Missing) 379036
77.1%
ValueCountFrequency (%)
0 283
 
0.1%
1 70456
14.3%
2 19366
 
3.9%
3 8292
 
1.7%
4 4319
 
0.9%
5 2304
 
0.5%
6 1656
 
0.3%
7 1202
 
0.2%
8 1015
 
0.2%
9 784
 
0.2%
ValueCountFrequency (%)
684 1
 
< 0.1%
586 4
< 0.1%
584 1
 
< 0.1%
552 1
 
< 0.1%
531 1
 
< 0.1%
474 1
 
< 0.1%
455 1
 
< 0.1%
435 1
 
< 0.1%
428 1
 
< 0.1%
426 1
 
< 0.1%

Количество соучредителей
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct32
Distinct (%)< 0.1%
Missing379036
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean1.2683418
Minimum0
Maximum656
Zeros758
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:29.687961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum656
Range656
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8889141
Coefficient of variation (CV)2.2777094
Kurtosis46854.862
Mean1.2683418
Median Absolute Deviation (MAD)0
Skewness207.09847
Sum142847
Variance8.3458248
MonotonicityNot monotonic
2025-05-24T16:26:29.750480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 92563
 
18.8%
2 13677
 
2.8%
3 3767
 
0.8%
4 926
 
0.2%
0 758
 
0.2%
5 395
 
0.1%
6 238
 
< 0.1%
7 70
 
< 0.1%
10 59
 
< 0.1%
8 55
 
< 0.1%
Other values (22) 117
 
< 0.1%
(Missing) 379036
77.1%
ValueCountFrequency (%)
0 758
 
0.2%
1 92563
18.8%
2 13677
 
2.8%
3 3767
 
0.8%
4 926
 
0.2%
5 395
 
0.1%
6 238
 
< 0.1%
7 70
 
< 0.1%
8 55
 
< 0.1%
9 34
 
< 0.1%
ValueCountFrequency (%)
656 2
< 0.1%
71 1
 
< 0.1%
45 1
 
< 0.1%
38 1
 
< 0.1%
32 4
< 0.1%
31 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%

Количество филиалов
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing379036
Missing (%)77.1%
Memory size3.8 MiB
0.0
112625 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters337875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 112625
 
22.9%
(Missing) 379036
77.1%

Length

2025-05-24T16:26:29.811595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:29.856238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 112625
100.0%

Most occurring characters

ValueCountFrequency (%)
0 225250
66.7%
. 112625
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 337875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 225250
66.7%
. 112625
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 337875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 225250
66.7%
. 112625
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 337875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 225250
66.7%
. 112625
33.3%

Компания входит в: Юридические лица, в состав исполнительных органов которых входят дисквалифицированные лица
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing298459
Missing (%)60.7%
Memory size3.8 MiB
Нет
193202 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters579606
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНет
2nd rowНет
3rd rowНет
4th rowНет
5th rowНет

Common Values

ValueCountFrequency (%)
Нет 193202
39.3%
(Missing) 298459
60.7%

Length

2025-05-24T16:26:29.901501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:29.948077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
нет 193202
100.0%

Most occurring characters

ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 579606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Н 193202
33.3%
е 193202
33.3%
т 193202
33.3%

Кредиты
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing295964
Missing (%)60.2%
Memory size3.8 MiB
False
185137 
True
 
10560
(Missing)
295964 
ValueCountFrequency (%)
False 185137
37.7%
True 10560
 
2.1%
(Missing) 295964
60.2%
2025-05-24T16:26:29.990848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

ОПФ Организации
Text

Missing 

Distinct92
Distinct (%)< 0.1%
Missing298459
Missing (%)60.7%
Memory size3.8 MiB
2025-05-24T16:26:30.127510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length122
Median length43
Mean length39.992438
Min length1

Characters and Unicode

Total characters7726619
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)< 0.1%

Sample

1st row65-Общества с ограниченной ответственностью
2nd row50102-Индивидуальные предприниматели
3rd row65-Общества с ограниченной ответственностью
4th row65-Общества с ограниченной ответственностью
5th row65-Общества с ограниченной ответственностью
ValueCountFrequency (%)
с 110220
18.1%
ограниченной 110220
18.1%
ответственностью 110220
18.1%
65-общества 109963
18.0%
50102-индивидуальные 79626
13.0%
предприниматели 79626
13.0%
50101-главы 856
 
0.1%
крестьянских 856
 
0.1%
фермерских 856
 
0.1%
хозяйств 856
 
0.1%
Other values (144) 7133
 
1.2%
2025-05-24T16:26:30.352347image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
н 796321
 
10.3%
е 691608
 
9.0%
т 635936
 
8.2%
и 516927
 
6.7%
о 447736
 
5.8%
с 447551
 
5.8%
417256
 
5.4%
в 414894
 
5.4%
а 384810
 
5.0%
р 275378
 
3.6%
Other values (52) 2698202
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7726619
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
н 796321
 
10.3%
е 691608
 
9.0%
т 635936
 
8.2%
и 516927
 
6.7%
о 447736
 
5.8%
с 447551
 
5.8%
417256
 
5.4%
в 414894
 
5.4%
а 384810
 
5.0%
р 275378
 
3.6%
Other values (52) 2698202
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7726619
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
н 796321
 
10.3%
е 691608
 
9.0%
т 635936
 
8.2%
и 516927
 
6.7%
о 447736
 
5.8%
с 447551
 
5.8%
417256
 
5.4%
в 414894
 
5.4%
а 384810
 
5.0%
р 275378
 
3.6%
Other values (52) 2698202
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7726619
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
н 796321
 
10.3%
е 691608
 
9.0%
т 635936
 
8.2%
и 516927
 
6.7%
о 447736
 
5.8%
с 447551
 
5.8%
417256
 
5.4%
в 414894
 
5.4%
а 384810
 
5.0%
р 275378
 
3.6%
Other values (52) 2698202
34.9%

Отчетный период (год)
Real number (ℝ)

High correlation  Missing 

Distinct19
Distinct (%)< 0.1%
Missing447784
Missing (%)91.1%
Infinite0
Infinite (%)0.0%
Mean2016.7226
Minimum2000
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:30.410751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2015
Q12017
median2017
Q32017
95-th percentile2017
Maximum2018
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1498656
Coefficient of variation (CV)0.00057016549
Kurtosis84.903645
Mean2016.7226
Median Absolute Deviation (MAD)0
Skewness-7.9556509
Sum88487739
Variance1.322191
MonotonicityNot monotonic
2025-05-24T16:26:30.470909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2017 34526
 
7.0%
2016 5654
 
1.1%
2018 1501
 
0.3%
2015 1440
 
0.3%
2014 214
 
< 0.1%
2013 181
 
< 0.1%
2002 53
 
< 0.1%
2006 43
 
< 0.1%
2008 42
 
< 0.1%
2012 37
 
< 0.1%
Other values (9) 186
 
< 0.1%
(Missing) 447784
91.1%
ValueCountFrequency (%)
2000 9
 
< 0.1%
2001 13
 
< 0.1%
2002 53
< 0.1%
2003 14
 
< 0.1%
2004 21
 
< 0.1%
2005 16
 
< 0.1%
2006 43
< 0.1%
2007 34
< 0.1%
2008 42
< 0.1%
2009 37
< 0.1%
ValueCountFrequency (%)
2018 1501
 
0.3%
2017 34526
7.0%
2016 5654
 
1.1%
2015 1440
 
0.3%
2014 214
 
< 0.1%
2013 181
 
< 0.1%
2012 37
 
< 0.1%
2011 12
 
< 0.1%
2010 30
 
< 0.1%
2009 37
 
< 0.1%

Пассивы всего
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct24926
Distinct (%)57.7%
Missing448457
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean48887463
Minimum-357120
Maximum3.3024064 × 1010
Zeros0
Zeros (%)0.0%
Negative14
Negative (%)< 0.1%
Memory size3.8 MiB
2025-05-24T16:26:30.536515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-357120
5-th percentile10000
Q1633350
median5545060
Q329853622
95-th percentile1.8066915 × 108
Maximum3.3024064 × 1010
Range3.3024421 × 1010
Interquartile range (IQR)29220272

Descriptive statistics

Standard deviation3.0975083 × 108
Coefficient of variation (CV)6.3359972
Kurtosis4791.187
Mean48887463
Median Absolute Deviation (MAD)5528460
Skewness54.13234
Sum2.112134 × 1012
Variance9.5945575 × 1016
MonotonicityNot monotonic
2025-05-24T16:26:30.607642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40457730 132
 
< 0.1%
10600 120
 
< 0.1%
8700 102
 
< 0.1%
12000 94
 
< 0.1%
10400 94
 
< 0.1%
9700 94
 
< 0.1%
8800 94
 
< 0.1%
8200 93
 
< 0.1%
9600 91
 
< 0.1%
10300 89
 
< 0.1%
Other values (24916) 42201
 
8.6%
(Missing) 448457
91.2%
ValueCountFrequency (%)
-357120 1
< 0.1%
-289100 1
< 0.1%
-123420 1
< 0.1%
-106480 1
< 0.1%
-86520 1
< 0.1%
-64000 1
< 0.1%
-34500 2
< 0.1%
-10000 1
< 0.1%
-9020 1
< 0.1%
-8300 1
< 0.1%
ValueCountFrequency (%)
3.302406377 × 10101
 
< 0.1%
2.839452212 × 10101
 
< 0.1%
1.140015111 × 10101
 
< 0.1%
1.044093257 × 10101
 
< 0.1%
1.0391359 × 10101
 
< 0.1%
8683547880 1
 
< 0.1%
6982807050 1
 
< 0.1%
6474986850 1
 
< 0.1%
6392496040 4
< 0.1%
6202619920 4
< 0.1%

Продукт открыт ранее
Boolean

Distinct2
Distinct (%)< 0.1%
Missing126
Missing (%)< 0.1%
Memory size3.8 MiB
False
375228 
True
116307 
(Missing)
 
126
ValueCountFrequency (%)
False 375228
76.3%
True 116307
 
23.7%
(Missing) 126
 
< 0.1%
2025-05-24T16:26:30.663612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Продукт ранее закрыт
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing126
Missing (%)< 0.1%
Memory size3.8 MiB
False
485248 
True
 
6287
(Missing)
 
126
ValueCountFrequency (%)
False 485248
98.7%
True 6287
 
1.3%
(Missing) 126
 
< 0.1%
2025-05-24T16:26:30.703078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Субъект местонахождения
Text

Missing 

Distinct675
Distinct (%)0.3%
Missing296248
Missing (%)60.3%
Memory size3.8 MiB
2025-05-24T16:26:30.861895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters390826
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXG
2nd rowSJ
3rd rowKI
4th rowKI
5th rowMO
ValueCountFrequency (%)
sm 3039
 
1.6%
ax 2893
 
1.5%
aj 2755
 
1.4%
xw 1848
 
0.9%
qo 1737
 
0.9%
mu 1712
 
0.9%
jv 1649
 
0.8%
wc 1457
 
0.7%
gu 1404
 
0.7%
mx 1397
 
0.7%
Other values (665) 175522
89.8%
2025-05-24T16:26:31.089716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
J 22971
 
5.9%
X 21577
 
5.5%
M 20531
 
5.3%
G 16992
 
4.3%
R 16820
 
4.3%
P 16618
 
4.3%
C 16340
 
4.2%
Q 16314
 
4.2%
W 16285
 
4.2%
A 16024
 
4.1%
Other values (16) 210354
53.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 390826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
J 22971
 
5.9%
X 21577
 
5.5%
M 20531
 
5.3%
G 16992
 
4.3%
R 16820
 
4.3%
P 16618
 
4.3%
C 16340
 
4.2%
Q 16314
 
4.2%
W 16285
 
4.2%
A 16024
 
4.1%
Other values (16) 210354
53.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 390826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
J 22971
 
5.9%
X 21577
 
5.5%
M 20531
 
5.3%
G 16992
 
4.3%
R 16820
 
4.3%
P 16618
 
4.3%
C 16340
 
4.2%
Q 16314
 
4.2%
W 16285
 
4.2%
A 16024
 
4.1%
Other values (16) 210354
53.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 390826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
J 22971
 
5.9%
X 21577
 
5.5%
M 20531
 
5.3%
G 16992
 
4.3%
R 16820
 
4.3%
P 16618
 
4.3%
C 16340
 
4.2%
Q 16314
 
4.2%
W 16285
 
4.2%
A 16024
 
4.1%
Other values (16) 210354
53.8%

Сумма налога
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct10292
Distinct (%)37.7%
Missing464390
Missing (%)94.5%
Infinite0
Infinite (%)0.0%
Mean1093797.4
Minimum-16892520
Maximum3.9263884 × 108
Zeros0
Zeros (%)0.0%
Negative51
Negative (%)< 0.1%
Memory size3.8 MiB
2025-05-24T16:26:31.160197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-16892520
5-th percentile3290
Q127060
median117000
Q3471280
95-th percentile3498600
Maximum3.9263884 × 108
Range4.0953136 × 108
Interquartile range (IQR)444220

Descriptive statistics

Standard deviation10420856
Coefficient of variation (CV)9.5272272
Kurtosis1049.538
Mean1093797.4
Median Absolute Deviation (MAD)107960
Skewness30.906197
Sum2.9828949 × 1010
Variance1.0859425 × 1014
MonotonicityNot monotonic
2025-05-24T16:26:31.229942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 56
 
< 0.1%
46440 48
 
< 0.1%
43200 45
 
< 0.1%
9700 44
 
< 0.1%
8200 44
 
< 0.1%
10300 42
 
< 0.1%
10800 40
 
< 0.1%
402400 36
 
< 0.1%
543240 36
 
< 0.1%
1961050 36
 
< 0.1%
Other values (10282) 26844
 
5.5%
(Missing) 464390
94.5%
ValueCountFrequency (%)
-16892520 1
< 0.1%
-15554730 1
< 0.1%
-4301000 1
< 0.1%
-4263600 1
< 0.1%
-3552120 1
< 0.1%
-1110510 1
< 0.1%
-1025640 1
< 0.1%
-914760 1
< 0.1%
-751540 1
< 0.1%
-731700 1
< 0.1%
ValueCountFrequency (%)
392638840 4
< 0.1%
380075390 1
 
< 0.1%
374118140 1
 
< 0.1%
370414000 4
< 0.1%
363005720 4
< 0.1%
344485020 1
 
< 0.1%
314851900 4
< 0.1%
214101650 1
 
< 0.1%
206085780 1
 
< 0.1%
201638700 1
 
< 0.1%

Тип организации
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing126
Missing (%)< 0.1%
Memory size3.8 MiB
ЮЛ
254447 
ИП
237088 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters983070
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowЮЛ
2nd rowЮЛ
3rd rowЮЛ
4th rowИП
5th rowИП

Common Values

ValueCountFrequency (%)
ЮЛ 254447
51.8%
ИП 237088
48.2%
(Missing) 126
 
< 0.1%

Length

2025-05-24T16:26:31.292670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T16:26:31.336792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
юл 254447
51.8%
ип 237088
48.2%

Most occurring characters

ValueCountFrequency (%)
Ю 254447
25.9%
Л 254447
25.9%
И 237088
24.1%
П 237088
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 983070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Ю 254447
25.9%
Л 254447
25.9%
И 237088
24.1%
П 237088
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 983070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Ю 254447
25.9%
Л 254447
25.9%
И 237088
24.1%
П 237088
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 983070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Ю 254447
25.9%
Л 254447
25.9%
И 237088
24.1%
П 237088
24.1%

Участие в госконтрактах (год)
Real number (ℝ)

Missing 

Distinct8
Distinct (%)< 0.1%
Missing472114
Missing (%)96.0%
Infinite0
Infinite (%)0.0%
Mean2017.2949
Minimum2012
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:31.379196image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2014
Q12017
median2018
Q32018
95-th percentile2019
Maximum2019
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3319488
Coefficient of variation (CV)0.00066026481
Kurtosis1.2446919
Mean2017.2949
Median Absolute Deviation (MAD)1
Skewness-1.2320973
Sum39432063
Variance1.7740877
MonotonicityNot monotonic
2025-05-24T16:26:31.432198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2018 9302
 
1.9%
2017 3755
 
0.8%
2019 2163
 
0.4%
2016 2159
 
0.4%
2015 1155
 
0.2%
2014 674
 
0.1%
2013 322
 
0.1%
2012 17
 
< 0.1%
(Missing) 472114
96.0%
ValueCountFrequency (%)
2012 17
 
< 0.1%
2013 322
 
0.1%
2014 674
 
0.1%
2015 1155
 
0.2%
2016 2159
 
0.4%
2017 3755
0.8%
2018 9302
1.9%
2019 2163
 
0.4%
ValueCountFrequency (%)
2019 2163
 
0.4%
2018 9302
1.9%
2017 3755
0.8%
2016 2159
 
0.4%
2015 1155
 
0.2%
2014 674
 
0.1%
2013 322
 
0.1%
2012 17
 
< 0.1%

Участие в госконтрактах (количество)
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct57
Distinct (%)0.3%
Missing472114
Missing (%)96.0%
Infinite0
Infinite (%)0.0%
Mean1.6190208
Minimum0
Maximum147
Zeros8239
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:31.496790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum147
Range147
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.8039267
Coefficient of variation (CV)2.349523
Kurtosis238.81704
Mean1.6190208
Median Absolute Deviation (MAD)1
Skewness10.981913
Sum31647
Variance14.469858
MonotonicityNot monotonic
2025-05-24T16:26:31.570015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8239
 
1.7%
1 6131
 
1.2%
2 2118
 
0.4%
3 951
 
0.2%
4 458
 
0.1%
7 438
 
0.1%
5 270
 
0.1%
6 246
 
0.1%
8 122
 
< 0.1%
9 120
 
< 0.1%
Other values (47) 454
 
0.1%
(Missing) 472114
96.0%
ValueCountFrequency (%)
0 8239
1.7%
1 6131
1.2%
2 2118
 
0.4%
3 951
 
0.2%
4 458
 
0.1%
5 270
 
0.1%
6 246
 
0.1%
7 438
 
0.1%
8 122
 
< 0.1%
9 120
 
< 0.1%
ValueCountFrequency (%)
147 1
< 0.1%
109 1
< 0.1%
93 2
< 0.1%
88 1
< 0.1%
73 1
< 0.1%
70 1
< 0.1%
66 1
< 0.1%
59 1
< 0.1%
57 1
< 0.1%
55 2
< 0.1%

Численность компании
Categorical

High correlation  Imbalance  Missing 

Distinct12
Distinct (%)< 0.1%
Missing444225
Missing (%)90.4%
Memory size3.8 MiB
0 .. 5
31455 
16 .. 50
6367 
6 .. 10
4980 
11 .. 15
 
2441
51 .. 100
 
1467
Other values (7)
 
726

Length

Max length12
Median length6
Mean length6.6327262
Min length6

Characters and Unicode

Total characters314630
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0 .. 5
2nd row16 .. 50
3rd row0 .. 5
4th row6 .. 10
5th row6 .. 10

Common Values

ValueCountFrequency (%)
0 .. 5 31455
 
6.4%
16 .. 50 6367
 
1.3%
6 .. 10 4980
 
1.0%
11 .. 15 2441
 
0.5%
51 .. 100 1467
 
0.3%
101 .. 150 332
 
0.1%
201 .. 250 135
 
< 0.1%
251 .. 500 91
 
< 0.1%
151 .. 200 86
 
< 0.1%
501 .. 1000 51
 
< 0.1%
Other values (2) 31
 
< 0.1%
(Missing) 444225
90.4%

Length

2025-05-24T16:26:31.643253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
47435
33.3%
0 31455
22.1%
5 31455
22.1%
16 6367
 
4.5%
50 6367
 
4.5%
6 4980
 
3.5%
10 4980
 
3.5%
11 2441
 
1.7%
15 2441
 
1.7%
51 1467
 
1.0%
Other values (15) 2920
 
2.1%

Most occurring characters

ValueCountFrequency (%)
94872
30.2%
. 94870
30.2%
0 47381
15.1%
5 42547
13.5%
1 23160
 
7.4%
6 11347
 
3.6%
2 447
 
0.1%
е 2
 
< 0.1%
и 1
 
< 0.1%
б 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 314630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
94872
30.2%
. 94870
30.2%
0 47381
15.1%
5 42547
13.5%
1 23160
 
7.4%
6 11347
 
3.6%
2 447
 
0.1%
е 2
 
< 0.1%
и 1
 
< 0.1%
б 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 314630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
94872
30.2%
. 94870
30.2%
0 47381
15.1%
5 42547
13.5%
1 23160
 
7.4%
6 11347
 
3.6%
2 447
 
0.1%
е 2
 
< 0.1%
и 1
 
< 0.1%
б 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 314630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
94872
30.2%
. 94870
30.2%
0 47381
15.1%
5 42547
13.5%
1 23160
 
7.4%
6 11347
 
3.6%
2 447
 
0.1%
е 2
 
< 0.1%
и 1
 
< 0.1%
б 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Чистая прибыль (или убыток) компании
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct19584
Distinct (%)52.7%
Missing454534
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean4258717.9
Minimum-3.1078814 × 109
Maximum6.9617556 × 109
Zeros0
Zeros (%)0.0%
Negative7134
Negative (%)1.5%
Memory size3.8 MiB
2025-05-24T16:26:31.709095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-3.1078814 × 109
5-th percentile-1598802
Q115750
median274920
Q31969110
95-th percentile20628860
Maximum6.9617556 × 109
Range1.0069637 × 1010
Interquartile range (IQR)1953360

Descriptive statistics

Standard deviation55412727
Coefficient of variation (CV)13.011598
Kurtosis7234.088
Mean4258717.9
Median Absolute Deviation (MAD)433120
Skewness59.172519
Sum1.5811342 × 1011
Variance3.0705703 × 1015
MonotonicityNot monotonic
2025-05-24T16:26:31.780294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21971820 88
 
< 0.1%
20033130 88
 
< 0.1%
24772150 88
 
< 0.1%
-8566530 80
 
< 0.1%
17448210 44
 
< 0.1%
22402640 44
 
< 0.1%
18740670 44
 
< 0.1%
25202970 44
 
< 0.1%
25633790 44
 
< 0.1%
25849200 44
 
< 0.1%
Other values (19574) 36519
 
7.4%
(Missing) 454534
92.4%
ValueCountFrequency (%)
-3107881350 1
< 0.1%
-647392000 1
< 0.1%
-226486080 1
< 0.1%
-174964300 1
< 0.1%
-164304270 1
< 0.1%
-162792000 1
< 0.1%
-156929000 1
< 0.1%
-148903810 1
< 0.1%
-127875300 1
< 0.1%
-120919920 1
< 0.1%
ValueCountFrequency (%)
6961755600 1
 
< 0.1%
2051409600 1
 
< 0.1%
1670364920 1
 
< 0.1%
1473851400 4
< 0.1%
1430134000 1
 
< 0.1%
1375594640 4
< 0.1%
1373983660 1
 
< 0.1%
1263301200 4
< 0.1%
1235227840 4
< 0.1%
1221191160 1
 
< 0.1%

дата начала полномочий руководителя
Text

Missing 

Distinct16171
Distinct (%)14.4%
Missing379312
Missing (%)77.1%
Memory size3.8 MiB
2025-05-24T16:26:31.922303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length197
Median length10
Mean length14.390248
Min length10

Characters and Unicode

Total characters1616730
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8792 ?
Unique (%)7.8%

Sample

1st row29.05.2014;26.04.2013
2nd row11.09.2018
3rd row11.09.2018
4th row16.06.2014
5th row22.09.2011
ValueCountFrequency (%)
22.12.2009;12.10.2009 836
 
0.7%
19.03.2019 551
 
0.5%
22.03.2019 540
 
0.5%
26.02.2019 538
 
0.5%
28.01.2019 522
 
0.5%
26.03.2019 497
 
0.4%
11.03.2019 483
 
0.4%
11.02.2019 482
 
0.4%
06.03.2019 479
 
0.4%
06.02.2019 477
 
0.4%
Other values (16161) 106944
95.2%
2025-05-24T16:26:32.137895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 369030
22.8%
. 314378
19.4%
1 278158
17.2%
2 269478
16.7%
8 75768
 
4.7%
9 61821
 
3.8%
3 46584
 
2.9%
; 44840
 
2.8%
7 43667
 
2.7%
6 38956
 
2.4%
Other values (2) 74050
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1616730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 369030
22.8%
. 314378
19.4%
1 278158
17.2%
2 269478
16.7%
8 75768
 
4.7%
9 61821
 
3.8%
3 46584
 
2.9%
; 44840
 
2.8%
7 43667
 
2.7%
6 38956
 
2.4%
Other values (2) 74050
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1616730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 369030
22.8%
. 314378
19.4%
1 278158
17.2%
2 269478
16.7%
8 75768
 
4.7%
9 61821
 
3.8%
3 46584
 
2.9%
; 44840
 
2.8%
7 43667
 
2.7%
6 38956
 
2.4%
Other values (2) 74050
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1616730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 369030
22.8%
. 314378
19.4%
1 278158
17.2%
2 269478
16.7%
8 75768
 
4.7%
9 61821
 
3.8%
3 46584
 
2.9%
; 44840
 
2.8%
7 43667
 
2.7%
6 38956
 
2.4%
Other values (2) 74050
 
4.6%

день_заявки
Date

Distinct470
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
Minimum2018-06-18 00:00:00
Maximum2019-12-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-24T16:26:32.206384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:32.277027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

код основного оквэд
Text

Missing 

Distinct1630
Distinct (%)0.8%
Missing298779
Missing (%)60.8%
Memory size3.8 MiB
2025-05-24T16:26:32.441282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.3041186
Min length2

Characters and Unicode

Total characters1023069
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)0.1%

Sample

1st row46.73
2nd row43.21
3rd row46.72
4th row46.72
5th row68.20.1
ValueCountFrequency (%)
41.20 11965
 
6.2%
49.41 9148
 
4.7%
46.90 6150
 
3.2%
46.73 5781
 
3.0%
43.21 3895
 
2.0%
68.20 3589
 
1.9%
73.11 3390
 
1.8%
56.10 2645
 
1.4%
47.11 2506
 
1.3%
43.39 2332
 
1.2%
Other values (1620) 141481
73.4%
2025-05-24T16:26:32.673546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 225816
22.1%
4 152449
14.9%
1 133455
13.0%
2 107794
10.5%
6 78895
 
7.7%
3 75375
 
7.4%
9 70989
 
6.9%
0 62381
 
6.1%
7 58107
 
5.7%
5 33094
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1023069
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 225816
22.1%
4 152449
14.9%
1 133455
13.0%
2 107794
10.5%
6 78895
 
7.7%
3 75375
 
7.4%
9 70989
 
6.9%
0 62381
 
6.1%
7 58107
 
5.7%
5 33094
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1023069
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 225816
22.1%
4 152449
14.9%
1 133455
13.0%
2 107794
10.5%
6 78895
 
7.7%
3 75375
 
7.4%
9 70989
 
6.9%
0 62381
 
6.1%
7 58107
 
5.7%
5 33094
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1023069
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 225816
22.1%
4 152449
14.9%
1 133455
13.0%
2 107794
10.5%
6 78895
 
7.7%
3 75375
 
7.4%
9 70989
 
6.9%
0 62381
 
6.1%
7 58107
 
5.7%
5 33094
 
3.2%

размер уставного капитал ЮЛ
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct1256
Distinct (%)1.1%
Missing380788
Missing (%)77.4%
Infinite0
Infinite (%)0.0%
Mean1944097.3
Minimum10
Maximum1.551 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-05-24T16:26:32.744170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10000
Q110000
median10000
Q350000
95-th percentile250000
Maximum1.551 × 1011
Range1.551 × 1011
Interquartile range (IQR)40000

Descriptive statistics

Standard deviation4.6768698 × 108
Coefficient of variation (CV)240.56768
Kurtosis109095.68
Mean1944097.3
Median Absolute Deviation (MAD)0
Skewness329.10798
Sum2.155479 × 1011
Variance2.1873111 × 1017
MonotonicityNot monotonic
2025-05-24T16:26:32.820886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 61508
 
12.5%
100000 7285
 
1.5%
20000 6720
 
1.4%
50000 6211
 
1.3%
30000 4142
 
0.8%
15000 1993
 
0.4%
250000 1332
 
0.3%
200000 1241
 
0.3%
12000 1171
 
0.2%
25000 1146
 
0.2%
Other values (1246) 18124
 
3.7%
(Missing) 380788
77.4%
ValueCountFrequency (%)
10 3
 
< 0.1%
12 1
 
< 0.1%
20 1
 
< 0.1%
80 1
 
< 0.1%
82 4
< 0.1%
100 1
 
< 0.1%
120 2
 
< 0.1%
300 1
 
< 0.1%
500 8
< 0.1%
1000 1
 
< 0.1%
ValueCountFrequency (%)
1.551 × 10111
 
< 0.1%
1.147085 × 10101
 
< 0.1%
4700000000 1
 
< 0.1%
3039942000 3
< 0.1%
1691717000 1
 
< 0.1%
1358205494 1
 
< 0.1%
1249869000 2
< 0.1%
1000000000 1
 
< 0.1%
920087422 1
 
< 0.1%
905149000 1
 
< 0.1%

no_reports
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size480.3 KiB
True
296090 
False
195571 
ValueCountFrequency (%)
True 296090
60.2%
False 195571
39.8%
2025-05-24T16:26:32.881542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Interactions

2025-05-24T16:26:13.652956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.227183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.679722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.204448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.678119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.081316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.696672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.221698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.071902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.559435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.015312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.546687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.114483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.650606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.101381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.538032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:00.093607image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.934815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.478088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.924037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.385205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.890973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.286098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.743469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.239355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.711131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.286865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.753899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.264362image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.739027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.144999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.752884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.280938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.126064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.611436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.069612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.600929image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.171237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.706527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.156631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.594372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:00.153736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.992734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.532858image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.980483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.441397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.945308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.345274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.800660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.295720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.768822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.343890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.809129image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.322618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.797923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.208312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.807596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.341031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.179553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.664147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.124374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.655535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.227697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.762273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.211074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.651252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:00.213502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.050586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.587685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.037398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.497354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.014316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.403004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.858331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.349790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.830571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.404212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.870371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.383052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.856719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.273891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.869890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.403340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.236822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.719815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.186282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.713681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.284603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.820997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.279922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.710839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:00.275015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.111885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.646104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.097393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.556733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.077608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.464917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.918217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.409513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.888659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.467171image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.932497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.439323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.908254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.336303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.928735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.462241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.291558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.773627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.243524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.769428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.339881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.880015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.335704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.768116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:00.334099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.170829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.702201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.153538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.611132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.138832image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.521007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.976042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.470574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.962729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.533108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.998982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.506550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.972554image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.405989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.996064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.533269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.358087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.837192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.315393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.835524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.408479image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.948251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.403945image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.836194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:00.403602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.242473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.770009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.221145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.677038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.206861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.588492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.042861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.536677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.027120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.589478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.055066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.568556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.030357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.470953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.052815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.595926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.417615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.895923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.378095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.896078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.469436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.007456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.461821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.898069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:00.467113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.304749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.828179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.279375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.733435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.266589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.650008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.103931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.592299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.094387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.651682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.117870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.630885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.090004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.539405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.116245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.661828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.491344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.967606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.459674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.971001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.547321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.070473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.523403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.965244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:00.533388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.371442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.890401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.341345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.793304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.319557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.711649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.166054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.652490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.151622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.706044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.171548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.687094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.141136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.601009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.171992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.721983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.559274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.030813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.533183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.039052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.616462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.126072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.576261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.024698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:00.895345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.430444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.944873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.396213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.846479image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.370049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.765949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.220448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.705407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.208666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.758354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.222908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.740775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.191699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.660975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.226037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.780786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.623911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.093539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.600299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.104666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.682091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.178892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.629605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.082225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:00.955141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.488387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.998136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.449158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.958232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.418320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.819015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.273192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.755161image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.266863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.813180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.277098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.796663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.244107image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.724013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.281719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.842039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.692444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.162281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.669615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.171622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.754564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.234713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.684563image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.142665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.015943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.548538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.053889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.505860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.012644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.471276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.875121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.326844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.807432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.327800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.869118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.332983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.854126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.298547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.787550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.339971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.904507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.763213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.229358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.745112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.244100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.824259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.290794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.740793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.203714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.079620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.610019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.110636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.562600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.069964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.525186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.932278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.383270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.860366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.391440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.928634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.462287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.909485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.350725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.854168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.458746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.968476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.821585image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.286709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.801957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.302658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.884098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.350762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.799773image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.266237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.141458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.674263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.170602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.619792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.125266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.578081image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.987711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.437599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.913848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.450642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:37.984045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.517893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.966461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.404543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.917543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.515242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:47.028779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.876906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.341508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.858157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.359741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.941909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.405430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.854075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.323121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.200702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.734674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.228111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.676206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.180555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.630746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.044054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.493026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.967409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.510502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.039815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.572845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.023160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.457547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.980350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.571282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:47.090913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.934407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.399482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.914449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.476088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.004000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.462188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.907885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.382574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.261282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.795518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.285849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.733118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.236975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.682460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.099476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.547652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.020814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.572176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.098334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.630433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.084056image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.514794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.045640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.630548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:47.158240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.995677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.461454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:51.977011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.538864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.068740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.521523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.965494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.446198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.327416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.857715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.344120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.791662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.296659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.733242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.158999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.606043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.076837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.639193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.160747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.692577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.147121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.572703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.114074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.694502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:47.239888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.056326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.520043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.035944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.601016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.133636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.583355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.025555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.509765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.390015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.924457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.405859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.854191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.359490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.785947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.219341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.713728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.136574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.705527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.218736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.750842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.206842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.629746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.184145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.758816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:47.591110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.117945image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.580811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.098075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.664190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.198323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.647117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.088739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.573270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.457309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:02.989879image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.467558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.918716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.422526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.843636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.279010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.781422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.199368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.767017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.274728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.805821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.265280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.685261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.247026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.814822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:47.651727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.173828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.635437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.154387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.719705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.257057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.704796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.146461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.630486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.517551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.051750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.523289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:05.977582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.482208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.894550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.335810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.837739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.257664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.829830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.333060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.863387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.325644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.740952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.313261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.874653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:47.714616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.230871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.692039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.215711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.778189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.314871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.763746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.204705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.690226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.579079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.115444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.583250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.037922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.543855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:08.957886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.398478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.896942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.316283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.887603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.391765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.922127image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.384350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.796700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.377141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.930485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:47.774459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.285231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.745574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.269938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.833860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.369311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.819617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.259744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.748439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.638053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.176447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.640510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.095345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.599764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.021474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.456355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:11.954569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.371879image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:14.948030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.444670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:39.974102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.438371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.852389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.436751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:45.982376image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:47.826343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.334534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.793176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.320216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.884344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.419774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.870272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.309835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.799366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.690062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.231014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.689566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.149883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.652705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.070146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.509784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.009045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.424098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:15.008219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.504601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.033445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.500911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.910687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.502638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.042657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:47.888410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.391184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.848734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.376716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.941955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.476504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.928541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.367025image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.859100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.751654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.292381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.747734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.209252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.716254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.126270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.569148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.068825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.482986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:15.067368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.566031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.091741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.561209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:42.968273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.567544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.104372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:47.949749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.447411image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.904407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.433217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:53.999767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.535318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:56.985021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.424041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:59.917877image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.812000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.352856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.806173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.268993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.775813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.182755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.628992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.126516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.542193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:15.123526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:38.620632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:40.146013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:41.618469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:43.025442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:44.629970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:46.159992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:48.008187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:49.500291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:50.958688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:52.487606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:54.054129image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:55.589530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:57.041919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:25:58.478344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:00.032144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:01.870208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:03.412656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:04.862187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:06.324609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:07.830519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:09.234949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:10.684128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:12.181514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-24T16:26:13.594694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-05-24T16:26:32.948565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2 - В учредителях/участниках/ акционерах клиента участие государства более 5025 - Государственные заказы и контракты Количество заключенных контрактов25 - Государственные заказы и контракты Приняла участие (количество)25 - Коммерческие заказы и контракты Количество заключенных контрактов25 - Коммерческие заказы и контракты Приняла участие (количество)6 - Организация, первое лицо, учредители входит в список «дисквалифицированных»7 - массовый контактный телефон: в заявке указан телефон, на который зарегистрировано более Х компаний (кроме компаний-агентов)8 - Количество компаний с аналогичным директором8 - Количество компаний с аналогичным директором в том же регионеIdInquiryhas_productshourmonthno_reportstargetweekdayЗаявки до датыИндекс должной осмотрительностиИндекс должной осмотрительности описаниеИндекс финансового рискаИндекс финансового риска описаниеКоличество видов деятельности у КлиентаКоличество компаний с аналогичным директором в том же регионеКоличество компаний, зарегистрированных на адресе регистрации Организации по данным сайта ФНСКоличество соучредителейКредитыОтчетный период (год)Пассивы всегоПродукт открыт ранееПродукт ранее закрытСумма налогаТип организацииУчастие в госконтрактах (год)Участие в госконтрактах (количество)Численность компанииЧистая прибыль (или убыток) компанииразмер уставного капитал ЮЛ
2 - В учредителях/участниках/ акционерах клиента участие государства более 501.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0020.0000.0010.0070.0000.0110.0190.0180.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0370.0000.0220.0000.000
25 - Государственные заказы и контракты Количество заключенных контрактов0.0001.0000.6660.2590.2611.0000.2790.0440.068-0.1930.0210.0230.1571.0000.0000.073-0.134-0.2060.026-0.2000.0000.0380.0680.1010.0090.0210.0340.0180.0200.0000.1050.0120.1560.2590.0210.006-0.106
25 - Государственные заказы и контракты Приняла участие (количество)0.0000.6661.0000.2180.2951.0000.2190.0340.041-0.2400.0260.0720.2341.0000.0000.046-0.045-0.1740.037-0.1220.0000.1300.0410.042-0.0360.0260.0090.0220.0020.0000.0560.0000.1710.2180.018-0.003-0.135
25 - Коммерческие заказы и контракты Количество заключенных контрактов0.0000.2590.2181.0000.5570.0000.1680.0890.093-0.2440.0310.0530.1530.0000.0000.061-0.132-0.0980.018-0.2300.0000.0630.0930.1080.0290.031-0.1280.1130.0260.0000.0820.0000.0691.0000.0450.006-0.014
25 - Коммерческие заказы и контракты Приняла участие (количество)0.0000.2610.2950.5571.0000.0000.1440.0550.105-0.1460.0610.0340.1130.0000.0000.069-0.061-0.1240.031-0.1710.0210.1130.1050.069-0.0100.061-0.0560.1240.0240.0000.0900.0000.2260.5570.0370.065-0.020
6 - Организация, первое лицо, учредители входит в список «дисквалифицированных»0.0001.0001.0000.0000.0001.0000.0000.0190.0230.0170.0080.0100.0190.0000.0080.0120.0000.0340.0170.0450.0070.0000.0230.0000.0000.0080.0260.0000.0110.0020.0000.0320.0000.0000.0180.0000.000
7 - массовый контактный телефон: в заявке указан телефон, на который зарегистрировано более Х компаний (кроме компаний-агентов)0.0000.2790.2190.1680.1440.0001.0000.0760.096-0.0220.047-0.0190.0570.0000.0090.0140.045-0.1490.009-0.4580.0160.0240.0960.1780.2170.0470.0320.2670.0060.0140.2401.0000.0790.1680.0270.102-0.098
8 - Количество компаний с аналогичным директором0.0000.0440.0340.0890.0550.0190.0761.0000.665-0.0010.0150.0330.0170.0000.0210.012-0.0030.0130.018-0.0140.0230.0260.6650.0960.0450.015-0.0280.0140.0160.0000.0201.000-0.0050.0890.031-0.046-0.016
8 - Количество компаний с аналогичным директором в том же регионе0.0000.0680.0410.0930.1050.0230.0960.6651.000-0.0360.0050.0400.0340.0000.0140.0260.0230.0260.023-0.0230.0530.3061.0000.1410.0450.005-0.0310.0320.0140.000-0.0070.0660.0500.0930.038-0.037-0.000
IdInquiry0.000-0.193-0.240-0.244-0.1460.017-0.022-0.001-0.0361.0000.0700.0530.5570.8950.1240.0450.1670.3400.0380.1690.1270.009-0.036-0.025-0.0180.0790.4180.0020.1560.095-0.0070.1110.098-0.2440.050-0.0110.007
has_products0.0000.0210.0260.0310.0610.0080.0470.0150.0050.0701.0000.0370.0750.0560.0730.0490.0150.3100.1940.3620.2090.0020.0050.0060.0000.9990.0580.0070.0000.0500.0210.0680.1770.0310.3710.0050.000
hour0.0140.0230.0720.0530.0340.010-0.0190.0330.0400.0530.0371.0000.0300.0600.020-0.0350.0610.0370.040-0.0160.0300.0530.040-0.0140.0000.059-0.0040.0200.1680.025-0.0100.046-0.0260.0530.030-0.0480.069
month0.0020.1570.2340.1530.1130.0190.0570.0170.0340.5570.0750.0301.0000.6600.0830.0500.020-0.3080.075-0.2050.1380.0090.0340.0250.0140.085-0.0260.0000.0500.0680.0250.0580.0330.1530.050-0.012-0.006
no_reports0.0001.0001.0000.0000.0000.0000.0000.0000.0000.8950.0560.0600.6601.0000.0000.0400.0670.0030.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0870.0460.0000.1040.0000.0000.0000.0000.000
target0.0010.0000.0000.0000.0000.0080.0090.0210.0140.1240.0730.0200.0830.0001.0000.0180.0430.2260.1200.1960.1330.0000.0140.0130.0000.0830.0300.0000.0200.0300.0000.1000.0460.0000.1400.0000.000
weekday0.0070.0730.0460.0610.0690.0120.0140.0120.0260.0450.049-0.0350.0500.0400.0181.0000.013-0.0210.052-0.0060.0380.0180.0260.0090.0170.092-0.0100.0080.0450.019-0.0010.0220.0520.0610.059-0.014-0.015
Заявки до даты0.000-0.134-0.045-0.132-0.0610.0000.045-0.0030.0230.1670.0150.0610.0200.0670.0430.0131.0000.0890.0110.0080.0230.0340.023-0.0200.0200.0250.1840.0060.0380.053-0.0130.0340.062-0.1320.0410.0310.012
Индекс должной осмотрительности0.011-0.206-0.174-0.098-0.1240.034-0.1490.0130.0260.3400.3100.037-0.3080.0030.226-0.0210.0891.0000.8970.3730.2590.0500.0260.001-0.0570.310-0.106-0.2640.1120.064-0.3641.000-0.069-0.0980.112-0.562-0.025
Индекс должной осмотрительности описание0.0190.0260.0370.0180.0310.0170.0090.0180.0230.0380.1940.0400.0750.0000.1200.0520.0110.8971.0000.2670.3200.0080.0230.0100.0260.1940.0900.0110.0440.0040.0401.0000.1270.0180.1470.0300.000
Индекс финансового риска0.018-0.200-0.122-0.230-0.1710.045-0.458-0.014-0.0230.1690.362-0.016-0.2050.0000.196-0.0060.0080.3730.2671.0000.9720.017-0.023-0.091-0.3340.362-0.070-0.5290.1100.063-0.4801.000-0.122-0.2300.179-0.350-0.306
Индекс финансового риска описание0.0100.0000.0000.0000.0210.0070.0160.0230.0530.1270.2090.0300.1380.0000.1330.0380.0230.2590.3200.9721.0000.0090.0530.0390.0140.2090.0880.0000.0140.0200.0091.0000.1050.0000.2290.0000.000
Количество видов деятельности у Клиента0.0000.0380.1300.0630.1130.0000.0240.0260.3060.0090.0020.0530.0090.0000.0000.0180.0340.0500.0080.0170.0091.0000.306-0.003-0.0230.0020.007-0.0380.0110.022-0.0730.0450.1030.0630.009-0.080-0.024
Количество компаний с аналогичным директором в том же регионе0.0000.0680.0410.0930.1050.0230.0960.6651.000-0.0360.0050.0400.0340.0000.0140.0260.0230.0260.023-0.0230.0530.3061.0000.1410.0450.005-0.0310.0320.0140.000-0.0070.0660.0500.0930.038-0.037-0.000
Количество компаний, зарегистрированных на адресе регистрации Организации по данным сайта ФНС0.0000.1010.0420.1080.0690.0000.1780.0960.141-0.0250.006-0.0140.0250.0000.0130.009-0.0200.0010.010-0.0910.039-0.0030.1411.0000.0670.006-0.0550.0930.0130.0000.0611.000-0.0250.1080.000-0.002-0.023
Количество соучредителей0.0000.009-0.0360.029-0.0100.0000.2170.0450.045-0.0180.0000.0000.0140.0000.0000.0170.020-0.0570.026-0.3340.014-0.0230.0450.0671.0000.000-0.0100.1230.0000.0000.1411.0000.0360.0290.0000.0350.007
Кредиты0.0000.0210.0260.0310.0610.0080.0470.0150.0050.0790.9990.0590.0850.0000.0830.0920.0250.3100.1940.3620.2090.0020.0050.0060.0001.0000.0570.0070.0250.0800.0210.0630.1760.0310.3720.0050.000
Отчетный период (год)0.0000.0340.009-0.128-0.0560.0260.032-0.028-0.0310.4180.058-0.004-0.0260.0340.030-0.0100.184-0.1060.090-0.0700.0880.007-0.031-0.055-0.0100.0571.0000.1190.0990.0370.1041.0000.126-0.1280.0250.0810.037
Пассивы всего0.0000.0180.0220.1130.1240.0000.2670.0140.0320.0020.0070.0200.0000.0000.0000.0080.006-0.2640.011-0.5290.000-0.0380.0320.0930.1230.0070.1191.0000.0110.0000.6251.0000.0830.1130.2950.4990.166
Продукт открыт ранее0.0000.0200.0020.0260.0240.0110.0060.0160.0140.1560.0000.1680.0500.0870.0200.0450.0380.1120.0440.1100.0140.0110.0140.0130.0000.0250.0990.0111.0000.1520.0250.1100.1470.0260.0230.0080.000
Продукт ранее закрыт0.0000.0000.0000.0000.0000.0020.0140.0000.0000.0950.0500.0250.0680.0460.0300.0190.0530.0640.0040.0630.0200.0220.0000.0000.0000.0800.0370.0000.1521.0000.0000.0370.2070.0000.0500.0000.000
Сумма налога0.0000.1050.0560.0820.0900.0000.2400.020-0.007-0.0070.021-0.0100.0250.0000.000-0.001-0.013-0.3640.040-0.4800.009-0.073-0.0070.0610.1410.0210.1040.6250.0250.0001.0001.0000.1440.0820.4280.7460.130
Тип организации0.0070.0120.0000.0000.0000.0321.0001.0000.0660.1110.0680.0460.0580.1040.1000.0220.0341.0001.0001.0001.0000.0450.0661.0001.0000.0631.0001.0000.1100.0371.0001.0000.1150.0001.0001.0001.000
Участие в госконтрактах (год)0.0370.1560.1710.0690.2260.0000.079-0.0050.0500.0980.177-0.0260.0330.0000.0460.0520.062-0.0690.127-0.1220.1050.1030.050-0.0250.0360.1760.1260.0830.1470.2070.1440.1151.0000.0690.0930.067-0.031
Участие в госконтрактах (количество)0.0000.2590.2181.0000.5570.0000.1680.0890.093-0.2440.0310.0530.1530.0000.0000.061-0.132-0.0980.018-0.2300.0000.0630.0930.1080.0290.031-0.1280.1130.0260.0000.0820.0000.0691.0000.0450.006-0.014
Численность компании0.0220.0210.0180.0450.0370.0180.0270.0310.0380.0500.3710.0300.0500.0000.1400.0590.0410.1120.1470.1790.2290.0090.0380.0000.0000.3720.0250.2950.0230.0500.4281.0000.0930.0451.0000.3271.000
Чистая прибыль (или убыток) компании0.0000.006-0.0030.0060.0650.0000.102-0.046-0.037-0.0110.005-0.048-0.0120.0000.000-0.0140.031-0.5620.030-0.3500.000-0.080-0.037-0.0020.0350.0050.0810.4990.0080.0000.7461.0000.0670.0060.3271.0000.099
размер уставного капитал ЮЛ0.000-0.106-0.135-0.014-0.0200.000-0.098-0.016-0.0000.0070.0000.069-0.0060.0000.000-0.0150.012-0.0250.000-0.3060.000-0.024-0.000-0.0230.0070.0000.0370.1660.0000.0000.1301.000-0.031-0.0141.0000.0991.000

Missing values

2025-05-24T16:26:15.540993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-24T16:26:16.874272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-24T16:26:22.023638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

17 - Юр. лица, имеющие задолженность по уплате налогов17 - Юр. лица, не предоставляющие налоговую отчетность более года2 - В учредителях/участниках/ акционерах клиента участие государства более 5025 - Государственные заказы и контракты Количество заключенных контрактов25 - Государственные заказы и контракты Приняла участие (количество)25 - Коммерческие заказы и контракты Количество заключенных контрактов25 - Коммерческие заказы и контракты Приняла участие (количество)4 - Перечень ОАО по Распоряжению Правительства № 91-Р4 - Перечень ФГУП, имеющих существенное значение4 - Реестр оборонно-промышленного комплекса4 - Реестр операторов, осуществляющих обработку персональных данных6 - Организация, первое лицо, учредители входит в список «дисквалифицированных»7 - массовый контактный телефон: в заявке указан телефон, на который зарегистрировано более Х компаний (кроме компаний-агентов)8 - Количество компаний с аналогичным директором8 - Количество компаний с аналогичным директором в том же регионеIdInquiryMonthhas_productshourmonthtargetweekdayВыручка компании (млн, руб)Дата блокировкиДата закрытияДата заявкиДата заявки_reportДата открытияДата регистрацииДаты внесения соучредителейЗаявки до датыИННИндекс должной осмотрительностиИндекс должной осмотрительности описаниеИндекс финансового рискаИндекс финансового риска описаниеИстория смены сооучредителейКоличество видов деятельности у КлиентаКоличество компаний с аналогичным директором в том же регионеКоличество компаний, зарегистрированных на адресе регистрации Организации по данным сайта ФНСКоличество соучредителейКоличество филиаловКомпания входит в: Юридические лица, в состав исполнительных органов которых входят дисквалифицированные лицаКредитыОПФ ОрганизацииОтчетный период (год)Пассивы всегоПродукт открыт ранееПродукт ранее закрытСубъект местонахожденияСумма налогаТип организацииУчастие в госконтрактах (год)Участие в госконтрактах (количество)Численность компанииЧистая прибыль (или убыток) компаниидата начала полномочий руководителядень_заявкикод основного оквэдразмер уставного капитал ЮЛno_reports
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN441465.02019-08False15.08.00.03.0NaNNaTNaT2019-08-22 15:06:00NaN2019-08-27NaNNaN0.0AAAAIRYZRCNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNЮЛNaNNaNNaNNaNNaN2019-08-22NaNNaNTrue
1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN441672.02019-08False16.08.00.03.0NaNNaTNaT2019-08-22 16:03:00NaN2019-08-27NaNNaN1.0AAAAIRYZRCNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNЮЛNaNNaNNaNNaNNaN2019-08-22NaNNaNTrue
2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN587893.02019-11False9.011.00.01.0NaNNaTNaT2019-11-26 09:20:00NaN2019-11-28NaNNaN0.0AAADCVZNURNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNЮЛNaNNaNNaNNaNNaN2019-11-26NaNNaNTrue
3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN549280.02019-10False15.010.00.02.0NaNNaTNaT2019-10-30 15:35:00NaN2019-10-31NaNNaN0.0AAAFBXICUBOONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNИПNaNNaNNaNNaNNaN2019-10-30NaNNaNTrue
4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN549481.02019-10False16.010.00.02.0NaNNaTNaT2019-10-30 16:17:00NaN2019-10-31NaNNaN1.0AAAFBXICUBOONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNИПNaNNaNNaNNaNNaN2019-10-30NaNNaNTrue
5NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN491762.02019-09False11.09.00.02.0NaNNaTNaT2019-09-25 11:25:00NaN2019-10-03NaNNaN0.0AAAHFBJQHKRENaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNИПNaNNaNNaNNaNNaN2019-09-25NaNNaNTrue
6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN493030.02019-09False16.09.00.02.0NaNNaTNaT2019-09-25 16:09:00NaN2019-10-03NaNNaN1.0AAAHFBJQHKRENaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNИПNaNNaNNaNNaNNaN2019-09-25NaNNaNTrue
7NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN386601.02019-07False15.07.00.01.0NaNNaTNaT2019-07-09 15:01:00NaN2019-07-09NaNNaN0.0AAAIPUKREPNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrueFalseNaNNaNЮЛNaNNaNNaNNaNNaN2019-07-09NaNNaNTrue
8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN360632.02019-06False14.06.01.00.0NaN2019-11-21NaT2019-06-17 14:18:00NaN2019-06-25NaNNaN0.0AAAKAMMOCANaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNЮЛNaNNaNNaNNaNNaN2019-06-17NaNNaNTrue
9NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN367617.02019-06False15.06.01.04.0NaN2019-11-21NaT2019-06-21 15:53:00NaN2019-06-25NaNNaN1.0AAAKAMMOCANaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNЮЛNaNNaNNaNNaNNaN2019-06-21NaNNaNTrue
17 - Юр. лица, имеющие задолженность по уплате налогов17 - Юр. лица, не предоставляющие налоговую отчетность более года2 - В учредителях/участниках/ акционерах клиента участие государства более 5025 - Государственные заказы и контракты Количество заключенных контрактов25 - Государственные заказы и контракты Приняла участие (количество)25 - Коммерческие заказы и контракты Количество заключенных контрактов25 - Коммерческие заказы и контракты Приняла участие (количество)4 - Перечень ОАО по Распоряжению Правительства № 91-Р4 - Перечень ФГУП, имеющих существенное значение4 - Реестр оборонно-промышленного комплекса4 - Реестр операторов, осуществляющих обработку персональных данных6 - Организация, первое лицо, учредители входит в список «дисквалифицированных»7 - массовый контактный телефон: в заявке указан телефон, на который зарегистрировано более Х компаний (кроме компаний-агентов)8 - Количество компаний с аналогичным директором8 - Количество компаний с аналогичным директором в том же регионеIdInquiryMonthhas_productshourmonthtargetweekdayВыручка компании (млн, руб)Дата блокировкиДата закрытияДата заявкиДата заявки_reportДата открытияДата регистрацииДаты внесения соучредителейЗаявки до датыИННИндекс должной осмотрительностиИндекс должной осмотрительности описаниеИндекс финансового рискаИндекс финансового риска описаниеИстория смены сооучредителейКоличество видов деятельности у КлиентаКоличество компаний с аналогичным директором в том же регионеКоличество компаний, зарегистрированных на адресе регистрации Организации по данным сайта ФНСКоличество соучредителейКоличество филиаловКомпания входит в: Юридические лица, в состав исполнительных органов которых входят дисквалифицированные лицаКредитыОПФ ОрганизацииОтчетный период (год)Пассивы всегоПродукт открыт ранееПродукт ранее закрытСубъект местонахожденияСумма налогаТип организацииУчастие в госконтрактах (год)Участие в госконтрактах (количество)Численность компанииЧистая прибыль (или убыток) компаниидата начала полномочий руководителядень_заявкикод основного оквэдразмер уставного капитал ЮЛno_reports
491651NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN414844.02019-08False9.08.00.04.0NaNNaTNaT2019-08-02 09:18:00NaN2019-08-05NaNNaN0.0ZZZWTREKWONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNЮЛNaNNaNNaNNaNNaN2019-08-02NaNNaNTrue
491652NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN415021.02019-08False10.08.00.04.0NaNNaTNaT2019-08-02 10:33:00NaN2019-08-05NaNNaN1.0ZZZWTREKWONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNЮЛNaNNaNNaNNaNNaN2019-08-02NaNNaNTrue
491653НетНетНетNaNNaNNaNNaNНетНетНетНетНетNaNNaN0.0175689.02018-09False12.09.00.00.0NaNNaTNaT2018-09-03 12:03:002018-09-03 12:01:21.333NaT24.08.2018NaN0.0ZZZWWZMAUGVDNaNNaNNaNNaNNaN12.00.0NaNNaNNaNНетFalse50102-Индивидуальные предпринимателиNaNNaNFalseFalseHNNaNИПNaNNaNNaNNaNNaN2018-09-0356.10NaNFalse
491654NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN371573.02019-06False9.06.00.02.0NaNNaTNaT2019-06-26 09:53:00NaN2019-06-26NaNNaN0.0ZZZXPHJRBCNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrueFalseNaNNaNЮЛNaNNaNNaNNaNNaN2019-06-26NaNNaNTrue
491655НетНетНетNaNNaNNaNNaNНетНетНетНетНетNaNNaN0.0232404.02018-12False10.012.00.04.0NaNNaT2019-04-242018-12-28 10:47:002018-12-28 10:42:48.3072019-01-2806.12.2017NaN0.0ZZZYJYKIWJUYNaNNaNNaNNaNNaN5.00.0NaNNaNNaNНетFalse50101-Главы крестьянских (фермерских) хозяйствNaNNaNFalseFalseYLNaNИПNaNNaNNaNNaNNaN2018-12-2801.45NaNFalse
491656НетНетНетNaNNaNNaNNaNНетНетНетНетНетNaNNaN0.0232404.02018-12False10.012.00.04.0NaNNaT2019-04-242018-12-28 10:47:002018-12-28 10:42:48.3072019-01-2806.12.2017NaN0.0ZZZYJYKIWJUYNaNNaNNaNNaNNaN5.00.0NaNNaNNaNНетFalse50101-Главы крестьянских (фермерских) хозяйствNaNNaNFalseFalseYLNaNИПNaNNaNNaNNaNNaN2018-12-2801.45NaNFalse
491657NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN521067.02019-10False12.010.00.00.0NaNNaTNaT2019-10-14 12:00:00NaN2019-10-15NaNNaN0.0ZZZZIBDUCCMFNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNИПNaNNaNNaNNaNNaN2019-10-14NaNNaNTrue
491658NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN521485.02019-10False13.010.00.00.0NaNNaTNaT2019-10-14 13:52:00NaN2019-10-15NaNNaN1.0ZZZZIBDUCCMFNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNИПNaNNaNNaNNaNNaN2019-10-14NaNNaNTrue
491659NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN549591.02019-10False16.010.00.02.0NaNNaTNaT2019-10-30 16:46:00NaN2019-10-31NaNNaN0.0ZZZZNXTPDFAGNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNИПNaNNaNNaNNaNNaN2019-10-30NaNNaNTrue
491660NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN551924.02019-10False16.010.00.03.0NaNNaTNaT2019-10-31 16:39:00NaN2019-10-31NaNNaN1.0ZZZZNXTPDFAGNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrueFalseNaNNaNИПNaNNaNNaNNaNNaN2019-10-31NaNNaNTrue

Duplicate rows

Most frequently occurring

17 - Юр. лица, имеющие задолженность по уплате налогов17 - Юр. лица, не предоставляющие налоговую отчетность более года2 - В учредителях/участниках/ акционерах клиента участие государства более 5025 - Государственные заказы и контракты Количество заключенных контрактов25 - Государственные заказы и контракты Приняла участие (количество)25 - Коммерческие заказы и контракты Количество заключенных контрактов25 - Коммерческие заказы и контракты Приняла участие (количество)4 - Перечень ОАО по Распоряжению Правительства № 91-Р4 - Перечень ФГУП, имеющих существенное значение4 - Реестр оборонно-промышленного комплекса4 - Реестр операторов, осуществляющих обработку персональных данных6 - Организация, первое лицо, учредители входит в список «дисквалифицированных»7 - массовый контактный телефон: в заявке указан телефон, на который зарегистрировано более Х компаний (кроме компаний-агентов)8 - Количество компаний с аналогичным директором8 - Количество компаний с аналогичным директором в том же регионеIdInquiryhas_productshourmonthtargetweekdayВыручка компании (млн, руб)Дата блокировкиДата закрытияДата заявки_reportДата открытияДата регистрацииДаты внесения соучредителейЗаявки до датыИННИндекс должной осмотрительностиИндекс должной осмотрительности описаниеИндекс финансового рискаИндекс финансового риска описаниеИстория смены сооучредителейКоличество видов деятельности у КлиентаКоличество компаний с аналогичным директором в том же регионеКоличество компаний, зарегистрированных на адресе регистрации Организации по данным сайта ФНСКоличество соучредителейКоличество филиаловКомпания входит в: Юридические лица, в состав исполнительных органов которых входят дисквалифицированные лицаКредитыОПФ ОрганизацииОтчетный период (год)Пассивы всегоПродукт открыт ранееПродукт ранее закрытСубъект местонахожденияСумма налогаТип организацииУчастие в госконтрактах (год)Участие в госконтрактах (количество)Численность компанииЧистая прибыль (или убыток) компаниидата начала полномочий руководителядень_заявкикод основного оквэдразмер уставного капитал ЮЛno_reports# duplicates
100НетНетНет0.01.00.01.0НетНетНетНетНет1.01.01.0258529.0True7.02.00.04.0121,70NaTNaT2019-02-22 06:58:58.1832019-03-1412.10.200926.07.2011;21.06.2011;12.10.20090.0AGLSEBFWDV12.0Низкий риск5.0Низкий рискЮЛ: 26.07.2011 0:00:00; ЮЛ: 21.06.2011 0:00:00; ЮЛ: 12.10.2009 0:00:006.01.01.01.00.0НетTrue65-Общества с ограниченной ответственностью2017.037121010.0FalseFalseQONaNЮЛ2017.00.016 .. 5022402640.022.12.2009;12.10.20092019-02-2241.220000.0False44
101НетНетНет0.01.00.01.0НетНетНетНетНет1.01.01.0258529.0True7.02.00.04.0121,70NaTNaT2019-02-22 07:09:37.5402019-03-1412.10.200926.07.2011;21.06.2011;12.10.20090.0AGLSEBFWDV12.0Низкий риск5.0Низкий рискЮЛ: 26.07.2011 0:00:00; ЮЛ: 21.06.2011 0:00:00; ЮЛ: 12.10.2009 0:00:006.01.01.01.00.0НетTrue65-Общества с ограниченной ответственностью2017.050050800.0FalseFalseQONaNЮЛ2017.00.016 .. 5021541000.022.12.2009;12.10.20092019-02-2241.220000.0False44
103НетНетНет0.01.00.01.0НетНетНетНетНет1.01.01.0268336.0True10.03.00.00.0121,70NaTNaT2019-03-11 10:56:53.7232019-03-1412.10.200926.07.2011;21.06.2011;12.10.20091.0AGLSEBFWDV12.0Низкий риск5.0Низкий рискЮЛ: 26.07.2011 0:00:00; ЮЛ: 21.06.2011 0:00:00; ЮЛ: 12.10.2009 0:00:006.01.01.01.00.0НетTrue65-Общества с ограниченной ответственностью2017.040457730.0FalseFalseQONaNЮЛ2017.00.016 .. 5025202970.022.12.2009;12.10.20092019-03-1141.220000.0False44
104НетНетНет0.01.00.01.0НетНетНетНетНет1.01.01.0268336.0True10.03.00.00.0121,70NaTNaT2019-03-11 12:24:43.6172019-03-1412.10.200926.07.2011;21.06.2011;12.10.20091.0AGLSEBFWDV12.0Низкий риск5.0Низкий рискЮЛ: 26.07.2011 0:00:00; ЮЛ: 21.06.2011 0:00:00; ЮЛ: 12.10.2009 0:00:006.01.01.01.00.0НетTrue65-Общества с ограниченной ответственностью2017.035035560.0FalseFalseQONaNЮЛ2017.00.016 .. 5025633790.022.12.2009;12.10.20092019-03-1141.220000.0False44
105НетНетНет0.01.00.01.0НетНетНетНетНет1.01.01.0268336.0True10.03.00.00.0121,70NaTNaT2019-03-11 12:25:32.8672019-03-1412.10.200926.07.2011;21.06.2011;12.10.20091.0AGLSEBFWDV12.0Низкий риск5.0Низкий рискЮЛ: 26.07.2011 0:00:00; ЮЛ: 21.06.2011 0:00:00; ЮЛ: 12.10.2009 0:00:006.01.01.01.00.0НетTrue65-Общества с ограниченной ответственностью2017.033367200.0FalseFalseQONaNЮЛ2017.00.016 .. 5021971820.022.12.2009;12.10.20092019-03-1141.220000.0False44
106НетНетНет0.01.00.01.0НетНетНетНетНет1.01.01.0268336.0True10.03.00.00.0121,70NaTNaT2019-03-11 12:32:33.4572019-03-1412.10.200926.07.2011;21.06.2011;12.10.20091.0AGLSEBFWDV12.0Низкий риск5.0Низкий рискЮЛ: 26.07.2011 0:00:00; ЮЛ: 21.06.2011 0:00:00; ЮЛ: 12.10.2009 0:00:006.01.01.01.00.0НетTrue65-Общества с ограниченной ответственностью2017.045879900.0FalseFalseQONaNЮЛ2017.00.016 .. 5018740670.022.12.2009;12.10.20092019-03-1141.220000.0False44
107НетНетНет0.01.00.01.0НетНетНетНетНет1.01.01.0268336.0True10.03.00.00.0121,70NaTNaT2019-03-11 12:36:18.7702019-03-1412.10.200926.07.2011;21.06.2011;12.10.20091.0AGLSEBFWDV12.0Низкий риск5.0Низкий рискЮЛ: 26.07.2011 0:00:00; ЮЛ: 21.06.2011 0:00:00; ЮЛ: 12.10.2009 0:00:006.01.01.01.00.0НетTrue65-Общества с ограниченной ответственностью2017.040874820.0FalseFalseQONaNЮЛ2017.00.016 .. 5025849200.022.12.2009;12.10.20092019-03-1141.220000.0False44
108НетНетНет0.01.00.01.0НетНетНетНетНет1.01.01.0268336.0True10.03.00.00.0121,70NaTNaT2019-03-11 12:46:26.9872019-03-1412.10.200926.07.2011;21.06.2011;12.10.20091.0AGLSEBFWDV12.0Низкий риск5.0Низкий рискЮЛ: 26.07.2011 0:00:00; ЮЛ: 21.06.2011 0:00:00; ЮЛ: 12.10.2009 0:00:006.01.01.01.00.0НетTrue65-Общества с ограниченной ответственностью2017.047131170.0FalseFalseQONaNЮЛ2017.00.016 .. 5024772150.022.12.2009;12.10.20092019-03-1141.220000.0False44
109НетНетНет0.01.00.01.0НетНетНетНетНет1.01.01.0268336.0True10.03.00.00.0121,70NaTNaT2019-03-11 13:20:45.9102019-03-1412.10.200926.07.2011;21.06.2011;12.10.20091.0AGLSEBFWDV12.0Низкий риск5.0Низкий рискЮЛ: 26.07.2011 0:00:00; ЮЛ: 21.06.2011 0:00:00; ЮЛ: 12.10.2009 0:00:006.01.01.01.00.0НетTrue65-Общества с ограниченной ответственностью2017.038789370.0FalseFalseQONaNЮЛ2017.00.016 .. 5021971820.022.12.2009;12.10.20092019-03-1141.220000.0False44
110НетНетНет0.01.00.01.0НетНетНетНетНет1.01.01.0268336.0True10.03.00.00.0121,70NaTNaT2019-03-11 13:28:08.9832019-03-1412.10.200926.07.2011;21.06.2011;12.10.20091.0AGLSEBFWDV12.0Низкий риск5.0Низкий рискЮЛ: 26.07.2011 0:00:00; ЮЛ: 21.06.2011 0:00:00; ЮЛ: 12.10.2009 0:00:006.01.01.01.00.0НетTrue65-Общества с ограниченной ответственностью2017.037538100.0FalseFalseQONaNЮЛ2017.00.016 .. 5024772150.022.12.2009;12.10.20092019-03-1141.220000.0False44